<h2 class="wp-block-heading" id="h-topline">TOPLINE</h2>



Although AI has become popular in recent months for its ability to perform advanced tasks and make life easier, there’s also increasing concern it can be used negatively, creating racial biases in the fields of healthcare, law enforcement and big tech.



<h2 class="wp-block-heading" id="h-key-facts">KEY FACTS</h2>



Joy Buolamwini, a researcher at MIT,&nbsp;<a href="https://time.com/5520558/artificial-intelligence-racial-gender-bias/" target="_blank" rel="noreferrer noopener">discovered</a>&nbsp;some facial analysis software was unable to detect her face until she covered her dark skin with a white mask, and chalked this up to AI systems’ lacking data as they’re largely trained on white faces.



A major U.S. technology company claimed its facial recognition accuracy rate was 97%, but through her research, Buolamwini&nbsp;<a href="https://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212" target="_blank" rel="noreferrer noopener">found</a>&nbsp;the company’s training set was more than 77% male and 83% white.



A&nbsp;<a href="https://www.hilarispublisher.com/open-access/face-verification-subject-to-varying-age-ethnicity-and-genderdemographics-using-deep-learning-2155-6180-1000323.pdf" target="_blank" rel="noreferrer noopener">report</a>&nbsp;published in the&nbsp;Journal of Biometrics and Biostatistics&nbsp;found Black women between the ages of 18 and 30 are the demographic with the poorest facial recognition accuracy.



A 2022&nbsp;<a href="https://dl.acm.org/doi/pdf/10.1145/3531146.3533138" target="_blank" rel="noreferrer noopener">study</a>&nbsp;conducted by institutions including Johns Hopkins University and the Georgia Institute of Technology programmed AI-trained robots to scan blocks with people’s faces from different races.



After the robots scanned the faces, they were tasked with designating which blocks were criminals—they consistently labeled the blocks with Black faces as criminals.



<h2 class="wp-block-heading" id="h-news-peg">NEWS PEG</h2>



In 2015, Google Photos came under fire after its AI-based search tool pulled images of Black people when the term “gorilla” was searched. After this controversy was revealed, Google stopped its software from categorizing any image as a “gorilla” and&nbsp;<a href="https://www.bbc.com/news/technology-33347866" target="_blank" rel="noreferrer noopener">promised</a>&nbsp;to fix the problem. However, after saving pictures of gorillas and different primates, a recent&nbsp;New York Times&nbsp;<a href="https://www.nytimes.com/2023/05/22/technology/ai-photo-labels-google-apple.html" target="_blank" rel="noreferrer noopener">report</a>&nbsp;found that the issue hadn’t been fixed, and Google Photos’ software was still programmed to prohibit generating any images categorized as gorillas. It wouldn’t generate images for searches of any primates.



<h2 class="wp-block-heading" id="h-crucial-quote">CRUCIAL QUOTE</h2>



“AI is just software that learns by example,” Reid Blackman, who has advised companies and governments on digital ethics and wrote the book&nbsp;Ethical Machines,&nbsp;<a href="https://www.cnn.com/2023/03/18/politics/ai-chatgpt-racist-what-matters/index.html" target="_blank" rel="noreferrer noopener">told</a>&nbsp;CNN. “So, if you give it examples that contain or reflect certain kinds of biases or discriminatory attitudes . . . you’re going to get outputs that resemble that.”



<h2 class="wp-block-heading" id="h-contra">CONTRA</h2>



According to a Pew Research&nbsp;<a href="https://www.pewresearch.org/short-reads/2023/04/20/most-americans-say-racial-bias-is-a-problem-in-the-workplace-can-ai-help/" target="_blank" rel="noreferrer noopener">poll</a>, 53% of Americans who believe racial and ethnic bias in the workplace is a problem think it would get better if employers used AI more in the hiring process, asserting that AI might potentially be beneficial in fighting against racial bias.



<h2 class="wp-block-heading" id="h-dermatology">DERMATOLOGY</h2>



According to a&nbsp;<a href="https://www.science.org/doi/10.1126/sciadv.abq6147" target="_blank" rel="noreferrer noopener">study</a>&nbsp;published in&nbsp;Science Advances,&nbsp;diagnostic and decision support tools used in dermatology are AI models trained on a dataset of skin tones and skin conditions. However, the dataset the AI is trained on is limited, as it lacks images of uncommon diseases, as well as diverse skin tones. A&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/33969523/" target="_blank" rel="noreferrer noopener">study</a>&nbsp;published in the&nbsp;Archives of Dermatological Research&nbsp;found that Covid disproportionately affects non-white ethnic groups. However, the dataset used to train dermatologists included 92% of pictures of Covid skin lesions on fairer skin tones, only 8% showed lesions on more olive skin tones, and none of them included dark skin tones. Because skin conditions show up differently in different skin colors, the need for diverse training is essential. For example, melanoma is rare in African Americans, according to a&nbsp;Journal of the American Academy of Dermatology&nbsp;<a href="https://www.jaad.org/article/S0190-9622(03)02091-7/fulltext" target="_blank" rel="noreferrer noopener">report</a>, but because training and education on dark skin tones in the field of dermatology is poor, it’s more likely to go undiagnosed than in white Americans, leading to a drastically lower survival rate. A 2019&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/31157429/" target="_blank" rel="noreferrer noopener">study</a>&nbsp;published in the&nbsp;British Journal of Dermatology&nbsp;found 47% of dermatologists felt they weren’t adequately trained to diagnose skin disease in people of color.



<h2 class="wp-block-heading" id="h-big-tech-companies">BIG TECH COMPANIES</h2>



In 2022, Apple was&nbsp;<a href="https://www.usatoday.com/story/tech/2022/12/27/apple-watch-blood-oxygen-oximeter-dark-skin-lawsuit/10955942002/" target="_blank" rel="noreferrer noopener">sued</a>&nbsp;over allegations that the Apple Watch’s blood oxygen sensor was racially biased against those with a darker skin tone. The Blood Oxygen app can measure oxygen levels in the blood by using sensors on the wrist, providing users with insights into their health, the app&nbsp;<a href="https://support.apple.com/en-us/HT211027" target="_blank" rel="noreferrer noopener">claims</a>. The lawsuit points to the Food and Drug Administration’s&nbsp;<a href="https://www.nejm.org/doi/10.1056/NEJMc2029240?url_ver=Z39.88-2003&amp;rfr_id=ori%3Arid%3Acrossref.org&amp;rfr_dat=cr_pub++0pubmed#article_citing_articles" target="_blank" rel="noreferrer noopener">review</a>&nbsp;of pulse oximeter technology created after the Covid pandemic increased its use in hospitals. The review found Black patients were almost three times more likely to have dangerously low blood oxygen levels go undetected by pulse oximetry compared to that of white patients. In an October 2022&nbsp;<a href="https://www.apple.com/healthcare/docs/site/Blood_Oxygen_app_on_Apple_Watch_October_2022.pdf" target="_blank" rel="noreferrer noopener">white paper</a>, Apple acknowledged the potential difficulties that come with pulse oximetry taking measurements on darker skin, but claimed its device “automatically adjusts . . . to ensure adequate signal resolution across the range of human skin tones.” In 2020, Twitter released its&nbsp;<a href="https://www.bbc.com/news/technology-57192898" target="_blank" rel="noreferrer noopener">photo cropping</a>&nbsp;feature, which crops and focuses on photos to allow for a better viewing experience, especially when scrolling through Twitter’s timeline. However, it was soon&nbsp;<a href="https://www.reuters.com/article/global-tech-race-twitter-idUKL5N2GI5R2" target="_blank" rel="noreferrer noopener">revealed</a>&nbsp;that the tool favored white faces, focusing on their faces in the photo preview and only revealing the Black faces when the users clicked to view the full photo. This led Twitter to&nbsp;<a href="https://blog.twitter.com/engineering/en_us/topics/insights/2021/sharing-learnings-about-our-image-cropping-algorithm" target="_blank" rel="noreferrer noopener">research</a>&nbsp;the algorithm, and it found in 2021 that it indeed had racial bias that favored white faces, leading to the company ditching the feature.



<h2 class="wp-block-heading" id="h-law-enforcement">LAW ENFORCEMENT</h2>



Facial recognition software is used in a number of different entities, including on smartphones, for police face recognition, airport passenger screening and employment decisions. However, the AI behind facial recognition has proven to be used to promote a racial bias.&nbsp;<a href="https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/" target="_blank" rel="noreferrer noopener">According</a>&nbsp;to Harvard’s Science in the News, police facial recognition software is trained on mugshot data, in which Black people are overrepresented. Racist policing strategies contribute to this, like the New York Police Department maintaining a&nbsp;<a href="https://www.stopspying.org/latest-news/2019/7/23/the-nypds-gang-database-a-new-age-of-stop-and-frisk" target="_blank" rel="noreferrer noopener">database</a>&nbsp;of “gang-affiliated” people made up of 99% Black and Latino people, though there are no requirements to prove these people are gang affiliated, thus incentivizing false reports.&nbsp;<a href="https://www.sciencedirect.com/science/article/abs/pii/S0740624X22000892?via%3Dihub" target="_blank" rel="noreferrer noopener">Research</a>&nbsp;published in&nbsp;Science Direct&nbsp;found law enforcement agencies that use AI-based facial recognition technology disproportionately arrest Black people due to their overwhelming presence in the training dataset.&nbsp;<a href="https://www.ajl.org/facial-recognition-technology" target="_blank" rel="noreferrer noopener">According</a>&nbsp;to the Algorithmic Justice League, “Face surveillance threatens rights including privacy, freedom of expression, freedom of association and due process.” Its controversial use in law enforcement has led to several cities across the country&nbsp;<a href="https://www.eff.org/deeplinks/2022/05/movement-ban-government-use-face-recognition" target="_blank" rel="noreferrer noopener">banning</a>&nbsp;police from using facial recognition.



<h2 class="wp-block-heading" id="h-related-targeting-ethnic-groups">RELATED: TARGETING ETHNIC GROUPS</h2>



Israeli authorities are relying on facial recognition to track Palestinians and restrict their passage through key checkpoints, according to a&nbsp;<a href="https://www.amnesty.org/en/documents/mde15/6701/2023/en/" target="_blank" rel="noreferrer noopener">report</a>&nbsp;by Amnesty International. The software, known as Red Wolf, is used at checkpoints, scanning Palestinians’ faces and using a color-coded system of either green, yellow or red to determine whether to allow them to pass, stop them for questioning or deny entry. This is just the latest example of Israel restricting Palestinians’ freedom of movement as the software heavily focuses on Palestinians, Amnesty argues, and the report calls it an “automated apartheid.” China has also used AI and facial recognition to identify minorities. The country came under fire for using the technology to racially profile the Uyghurs, a vastly Muslim minority group, the&nbsp;New York Times&nbsp;<a href="https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html" target="_blank" rel="noreferrer noopener">reports</a>. The facial recognition technology is embedded into China’s widespread network of surveillance cameras and exclusively looks for Uyghurs based on how they look, keeping a record of where they’re coming from—and going to. The U.S. even&nbsp;<a href="https://www.theguardian.com/world/2022/jan/06/china-sensetime-facial-recognition-uyghur-surveillance-us-sanctions" target="_blank" rel="noreferrer noopener">sanctioned</a>&nbsp;SenseTime, China’s largest facial recognition firm, for its alleged role in the surveillance of the Uyghurs.



<h2 class="wp-block-heading" id="h-further-reading">FURTHER READING</h2>



<a href="https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html" target="_blank" rel="noreferrer noopener">Google’s Photo App Still Can’t Find Gorillas. And Neither Can Apple’s</a>&nbsp;(New York Times)



<a href="https://www.forbes.com/sites/ariannajohnson/2023/04/14/youre-already-using-ai-heres-where-its-at-in-everyday-life-from-facial-recognition-to-navigation-apps/?sh=37c1cfb127ac">You’re Already Using AI: Here’s Where It’s At In Everyday Life, From Facial Recognition To Navigation Apps</a>&nbsp;(Forbes)



<a href="https://www.forbes.com/sites/ariannajohnson/2023/04/10/heres-how-to-use-ai-like-chatgpt-and-bard-for-everyday-tasks-like-creating-a-budget-finding-airfare-or-planning-meals/?sh=43627396190d">Here’s How To Use AI—Like ChatGPT And Bard—For Everyday Tasks Like Creating A Budget, Finding Airfare Or Planning Meals</a>&nbsp;(Forbes)



<a href="https://www.forbes.com/sites/ariannajohnson/2023/04/25/republicans-share-an-apocalyptic-ai-powered-attack-ad-against-biden-heres-how-to-spot-a-deepfake/?sh=4ac38d717753">Republicans Share An Apocalyptic AI-Powered Attack Ad Against Biden: Here’s How To Spot A Deepfake</a> (Forbes)

Students Working On Computer Assignment During Seminar Group

Racism And AI: Here’s How It’s Been Criticized For Amplifying Bias

Where does the continent fit into the chatbot conversation, and why it needs its own startups that focus on AI.



The ability to be able to have a conversation with someone else is a fundamental part of what it means to be a human being but today, it’s becoming more and more confusing to figure out whether you’re talking to a bot… or not.



Technology has advanced to the point where AI-powered conversational chatbots, like ChatGPT or Meta’s BlenderBot 3, are deceptively realistic. (More so when they go off the rails and say things like ‘I want to be alive’, which is exactly what Bing’s AI bot expressed earlier this year.) While chatbots like these are trained to avoid expressing personal or biased views, and are known to make factual mistakes and mix up information, they’re becoming smarter, more streamlined and sometimes, a little strange…



“Conversational AI is one of the first applications of NLP in artificial intelligence and this kind of technology has existed since Eliza in the sixties,” explains Professor Mpho Raborife, the deputy director at the Institute for Intelligent Systems at the University of Johannesburg in South Africa.



Natural Language Processing, or NLP, is a way for computers to understand human language. NLP uses AI tools like machine learning and neural networks to process and interpret large amounts of data.



“With the rise of big data, ChatGPT was inevitable. It’s trained on a very large dataset and the technologies behind it make it a lot more advanced than other conversational AI applications.”



One of the reasons for this, explains Raborife, is that ChatGPT uses reinforcement learning alongside sophisticated algorithms that manipulate its dataset. “It’s so sophisticated that people don’t realize that it really possesses no intelligence whatsoever,” she adds, “it really doesn’t understand the meaning behind anything, it just know how the words are supposed to work but not what they mean.”



OpenAI’s ChatGPT is a research experiment gone wild that has shown the incredible potential of conversational AI yet Africa is still being left behind. Consider smartphone voice assistants like Siri or Alexa – both examples of conversational AI used regularly; they work with English but struggle to understand local accents. They don’t support African languages.



“We know that the Global North is not going to include our languages. Therefore, we need to find a way to make sure that we create an environment that the startups that come from Africa which do incorporate our languages have the same kind of impact,” Raborife adds. Duolingo, for example, is a popular language learning mobile app that uses an AI system called Birdbrain to personalize its content. Duolingo has 43 different languages (including made-up dialects such as Klingon, High Valyrian and Esperanto) but only isiZulu and Swahili are available when it comes to African language learning courses.



“When we talk about AI, our languages are not supported or even represented by these technologies,” says Raborife. “But conversational AI has the potential to bridge the language divide as it can be used in a variety of ways because it surpasses literacy levels. If people cannot read or write, they can use their voice to access services and this is something that is important to Africa.



This is why we need our own startups that focus on AI. Recently launched, Lelapa AI is an African AI research and product house looking to make a real difference within the continent with a special interest in agriculture, education, energy, healthcare, microfinance and language.



“There are so many problems on the continent that AI-type technologies can help with so there’s a responsibility for us to use our skills to help create better lives for everyday Africans,” says Pelonomi Moiloa, Lelapa AI’s co-founder and CEO. 



“What we intend to do is use technology in a very smart, resource-efficient way because there are challenges on the continent like rolling blackouts, water shortages and network issues…” Lelapa AI have already debuted an app called VulaVula which offers NLP-as-a-service for under-represented languages. Moiloa describes VulaVula as an underlying technology that supports other technologies. A banking chatbot, for example, could use VulaVula’s AI language capabilities to help customers interact in their own languages.



“The idea is that if an organization has a place for us to plug our models into and we can collaborate agreements around how that work can be done, then – ideally – if we have the resources and capacity we will help businesses do what they need.”



For Moiloa, creating African data sets to work from may be challenging but it’s also something she sees as a competitive advantage for Lelapa AI. “We’re having to think around clever ways of dealing with Africa’s data scarcity problem. We have to create models that are able to cope with less amounts of data, but models that can get really good at curating and gathering data,” she says.



And the fact that ChatGPT doesn’t necessarily draw its information from African data sets (it was apparently trained using real human conversations) means startups like Lelapa AI have the opportunity to develop responsible methods of dealing with problems that are relevant to the continent.



“I don’t think we should be trying to develop our own ChatGPT but maybe we can develop other textual tools that can help solve the problems that ChatGPT is meant to – but at the moment, it seems to be creating more problems,” she ends.

AI (Artificial Intelligence) concept. Electronic circuit. Communication network.

Talking A-‘bot Africa: The Potential For Conversational AI

<h2 class="wp-block-heading">TOPLINE</h2>



Eric Schmidt, who served as Google’s CEO from 2001 to 2011, said Wednesday that artificial intelligence could cause people to be “harmed or killed” amid possible “existential risks,” adding to concerns from other tech executives over the development of AI.



<h2 class="wp-block-heading">KEY FACTS</h2>



Schmidt, who&nbsp;<a href="https://www.wsj.com/video/events/life-with-ai/F13F8BDE-2AB5-4BA1-9191-997FC338C1AF.html" target="_blank" rel="noreferrer noopener">spoke</a>&nbsp;during the&nbsp;Wall Street Journal’s CEO Council Summit Wednesday, warned AI poses risks that “is defined as many, many, many, many people harmed or killed.”



Schmidt—who also served as the executive chairman of Alphabet from 2015 to 2017—suggested regulations on AI is a “broader question for society,” adding he believes it is unlikely the U.S. will establish a regulatory agency.



Sundar Pichai, CEO of Google and Alphabet,&nbsp;<a href="https://www.ft.com/content/8be1a975-e5e0-417d-af51-78af17ef4b79" target="_blank" rel="noreferrer noopener">wrote</a>&nbsp;in the&nbsp;Financial Times&nbsp;that “AI is the most profound technology humanity is working on today,” adding it is important to “make sure as a society we get it right.”



Twitter CEO Elon Musk and Apple cofounder Steve Wozniak signed a&nbsp;<a href="https://futureoflife.org/open-letter/pause-giant-ai-experiments/" target="_blank" rel="noreferrer noopener">letter</a>&nbsp;in March alongside politician Andrew Yang, Skype cofounder Jaan Tallinn, Pinterest cofounder Evan Sharper and Ripple cofounder Chris Larson, which urged AI labs to “immediately pause” work to slow down an “out-of-control race” to develop the technology.



OpenAI CEO Sam Altman&nbsp;<a href="https://www.youtube.com/watch?v=mL5wI3tkXkw&amp;ab_channel=ABCNews" target="_blank" rel="noreferrer noopener">told</a>&nbsp;ABC News in March that his company was “a little bit scared” over AI’s potential, adding it “will be the greatest technology humanity has yet developed.”



<h2 class="wp-block-heading">CRUCIAL QUOTE</h2>



Schmidt warned, “There are scenarios—not today, but reasonably soon, where these systems will be able to find zero-day exploits in cyber issues, or discover new kinds of biology,” adding, “When that happens, we want to be ready to know how to make sure these things are misused by evil people.”



<h2 class="wp-block-heading">CONTRA</h2>



Billionaire philanthropist Bill Gates has applauded the possible impact AI could have on society, noting he has seen “stunning” advancements in recent months. Microsoft, which Gates founded in 1975, has&nbsp;<a href="https://www.forbes.com/sites/dereksaul/2023/01/23/chatgpt-parent-open-ai-gets-game-changing-multibillion-dollar-boost-from-microsoft/?sh=2f9a49cc3c0c">reportedly</a>&nbsp;invested $10 billion in OpenAI. Gates acknowledged the issues raised in the letter signed by Musk and others in a&nbsp;<a href="https://www.gatesnotes.com/The-Age-of-AI-Has-Begun" target="_blank" rel="noreferrer noopener">blog post</a>, though he noted any social concerns about the technology should be regulated by the government in an effort to ensure it was used for good. Gates also&nbsp;<a href="https://www.forbes.com/sites/anafaguy/2023/03/21/bill-gates-thinks-ai-will-revolutionize-healthcare-for-worlds-poorest/?sh=653e3470d192">suggested</a>&nbsp;AI could be used to improve productivity, reduce global preventable deaths among children and improve inequity in American education.



<h2 class="wp-block-heading">FORBES VALUATION</h2>



Schmidt, who also cofounded the venture capital firm Innovation Endeavors, is worth $20.1 billion, according to our&nbsp;<a href="https://www.forbes.com/profile/eric-schmidt/?sh=6e033446138e">estimates</a>.



<h2 class="wp-block-heading">KEY BACKGROUND</h2>



Schmidt’s warning follows his time on the National Security Commission on Artificial Intelligence, which released a&nbsp;<a href="https://www.nscai.gov/wp-content/uploads/2021/03/Full-Report-Digital-1.pdf" target="_blank" rel="noreferrer noopener">report</a>&nbsp;in 2021 that indicated the U.S. government was “not today prepared for this new technology.” Schmidt also called for increasing the nation’s budget for research and development to $2 billion in 2022, and then doubling contributions until it reached $32 billion in 2026. The White House&nbsp;<a href="https://www.whitehouse.gov/briefing-room/statements-releases/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/" target="_blank" rel="noreferrer noopener">released</a>&nbsp;a plan earlier this month to tackle possible risks from AI, including $140 million in funding to establish research institutes to drive responsible innovation. Calls for regulating AI have accelerated in recent months following the launch of OpenAI’s ChatGPT late last year. Other companies—including Google—have since released their own iterations of AI chatbots.



<h2 class="wp-block-heading">FURTHER READING</h2>



<a href="https://www.forbes.com/sites/roberthart/2023/03/29/elon-musk-and-tech-leaders-call-for-ai-pause-over-risks-to-humanity/?sh=5275dbea6dfc">Elon Musk And Tech Leaders Call For AI ‘Pause’ Over Risks To Humanity</a>&nbsp;(Forbes)



<a href="https://www.forbes.com/sites/alexkonrad/2023/02/06/bill-gates-openai-microsoft-ai-hottest-topic-2023/?sh=6a484d444777">Exclusive: Bill Gates On Advising OpenAI, Microsoft And Why AI Is ‘The Hottest Topic Of 2023’</a>&nbsp;(Forbes)



<a href="https://www.forbes.com/sites/anafaguy/2023/03/21/google-releases-bard-ai-chatbot-amid-competition-with-chatgpt/?sh=5c5f07bb32b7">Google Releases Bard AI Chatbot Amid Competition With ChatGPT</a> (Forbes)



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National Security Commission Holds Conference On Artificial Intelligence

Former Google CEO Warns AI Could Cause People To Be ‘Harmed Or Killed’

Despite the public roasting Levi’s took for flirting with AI, smaller clothing sellers who model their own products online maintain a growing interest in replacing themselves with the computer-generated kind.



On Tracy Porter’s&nbsp;clothing site, it’s all Tracy Porter.



Thin, blonde and 5-foot-10, the 55-year-old entrepreneur poses in each and every one of her designs. Her husband uses his iPhone to snap pictures, which they often take on the wildflower-strewn bluffs overlooking the beach 15 minutes from their home in California’s Santa Ynez Valley, before uploading them to their e-commerce site, The Porter Collective. There, you can see Porter in her striped midi dress, in her multicolor crocheted cardigan and in her black-and-white rattan visor.



Porter said she’s been searching for ways to replace herself as a model, not only to lighten the load but to bring some diversity to the site. Humans, however, are prohibitively expensive. So she’s been testing a new artificial-intelligence service from Israeli startup&nbsp;<a href="https://botika.io/" target="_blank" rel="noreferrer noopener">Botika</a>. She said the images look so realistic that when she showed her sons, they told her she was out of a job.



“I’m actually excited about that. I don’t want to be our model anymore,” Porter said with a laugh.



After Levi’s endured a scorching backlash in March for&nbsp;<a href="https://www.levistrauss.com/2023/03/22/lsco-partners-with-lalaland-ai/" target="_blank" rel="noreferrer noopener">its plan</a>&nbsp;to bring more diversity to the site with AI-generated models – critics flayed the company, which had 2022 sales of $6.2 billion, for not simply hiring more humans – it would be no surprise if big clothing retailers have gotten skittish about using, or admitting they use, nonhuman models. That same public disapproval hasn’t yet extended to the many mom-and-pop clothing shops, whose budgets, like Tracy Porter’s, would collapse under the costs of professional hair, makeup and photography – even without inflation, rising borrowing costs and a recession that feels as if it’s been looming forever.



Services like Botika let brands easily display their clothing on various (computer-generated) fashion models. However, they are still working to add more ages, body shapes and ethnicities.BOTIKA



“In the current economy, customers are mostly looking to optimize costs,” said Eran Dagan, cofounder and CEO of Botika, which caters to small businesses and plans to start subscriptions for its AI model service at just $15 a month. It’s preparing to launch on Shopify and has a waiting list of over 1,000 customers. Dagan said Botika has gotten interest from sellers on resale platforms like Poshmark and Depop, where entrepreneurs typically photograph clothing on themselves and crop out their heads.



Another company in the space,&nbsp;<a href="https://lalaland.ai/" target="_blank" rel="noreferrer noopener">Lalaland</a>&nbsp;— which is still set to power Levi’s attempt to go AI — said its recent growth has been driven mainly by small and medium-sized businesses. While the company declined to disclose revenue figures, cofounder and CEO Michael Musandu said business has picked up significantly over the last eight months and sales have grown by eight- or nine-fold.



“We’re helping emerging brands that are just starting out and have zero budgets, or low budgets, to actually plan photoshoots,” said Musandu. “We really help level the playing field by representing the underdog.”



The fact remains that for everyone, photoshoots are pricey, and while shoppers are increasingly demanding that clothes be shown on a variety of models, no brand has an unlimited budget to do that. Tracy Porter used to hire outside photographers, models and hair and makeup stylists to shoot her products, but it would cost $5,500 every time she brought them out. If she outsourced photography for her multimillion-dollar business today, she estimates it would set her back $500,000 a year. “It’s just too much money,” she said.



<blockquote class="wp-block-quote">
“What I’d like to do is take an image and see it on different sizes. Small, medium and large. But I don’t want to have to hire all the people to do that.”Tracy Porter, small business owner
</blockquote>



Which is how Porter landed on modeling the clothes herself. However, that makes it difficult for shoppers with a different body shape or ethnicity to get a sense for how her products would look on them. “What I’d like to do is take an image and see it on different sizes. Small, medium and large. But I don’t want to have to hire all the people to do that,” said Porter. “Because there will be no profit left in our company and we’ll have to close.”



Another small business owner, Jacob Flores, is looking forward to more easily finding models that reflect his customer base. The 50-year-old former web designer from San Antonio, whose online store Blissfully Brand sells skintight dresses, skater skirts and bell bottoms in colorful prints inspired by fashion from the 1960s and 1970s, has a lot of customers in their 40s and 50s. But many models for hire tend to be much younger.



Flores recently started using Lalaland, where he can hand-select digital models from a library, dress them in his products and upload the images to his site without leaving the house. For $300 a month, he gets 50 images, enabling him to display more of his clothing on models much faster. He was previously limited to doing photoshoots four times a year, where he would pay $900 to get as many shots of a few models in 20 outfits as he could in two hours.



Now he plans to do photoshoots just once or twice a year on location so he can show his clothing at the beach or on a city street. He also sees AI being helpful with social media ads, allowing him to target certain audiences with tailored images.



Richard Evans, founder of wellness startup Juro Miru, also opted to include some shots on AI models to launch his new e-commerce site, in addition to images of clothing laid on a flat surface.



Startup Juro Miru displays some of its products on AI models.SCREENSHOT OF JURO MIRU WEBSITE



As for shoppers, it can be impossible for them to tell whether they’re looking at a real or fake model. There are no clear rules for disclosure, although companies are beginning to think about it. TikTok is reportedly working on a tool that will give creators a way to say whether they’ve used generative AI.



“When you’re presenting an image that could easily be mistaken for an actual person, I think you should disclose that it’s a computer-generated image,” said David Danks, a professor at the University of California, San Diego whose research explores the ethical issues around artificial intelligence.



Porter is wrestling with this question, among others. “How would you even know? Do we have to say? Are we supposed to say? There are a lot of questions,” she said. Her friends seem afraid of AI and have told her they think it will put models out of work. “It’s a constant dinner table conversation for us.”



<a href="https://www.forbes.com/sites/laurendebter/"></a>By Lauren Debter, Forbes Staff

Pacific Islander woman wearing artificial technology mask

Clothing Entrepreneurs Can’t Wait To Fire Themselves As Models And Hire The AI Kind Instead

A new report released by Oxford Insights shows that even though there is a digital divide as well as data poverty in Africa, five countries on the continent are taking leaps into leveraging technologies to accelerate economic growth with the use of Artificial Intelligence (AI).&nbsp;



In the 2020 Government AI Readiness Index Mauritius took the 45th position globally out of 172 countries across the world, making it the highest-ranked African country on the index. According to <a href="https://www.youtube.com/watch?v=1MEWNMV6MQI&amp;feature=youtu.be">Oxford Insights</a>, Mauritius has always been forward-looking in terms of AI as it is is the only country on the continent to have published an AI strategy.&nbsp;



“Mauritius approach to AI is fantastic… They have a clear strategy as to what they want to achieve. They are also bringing the private sector and academia with them. They have a council with all of these sectors on them. They are investing in the skills and they are thinking about how that can be transferred back into the industry. That’s amazing and that’s the perfect playbook,” Richard Stirling, Oxford Insights CEO says. &nbsp;



The methodology of this index looks at a countries’ government’s willingness to adopt AI and if the country has a good supply of AI expertise and tools from the technology sector. In addition to that part, these tools need to be built and trained on high-quality and representative data with the appropriate infrastructure to be delivered to and used by citizens. 



Mauritius is also ranked 13 out of 34 countries on this year’s Responsible Use Sub-Index. This index, also by Oxford Insights, tracks the ethical use of AI in countries across the globe. The debate around responsible AI was put into focus this year due to the Covid-19 pandemic, issues around privacy as well as ongoing global inequality. The index was measured using inclusivity, accountability, transparency, and privacy as some of its main indicators.&nbsp;



“Just because you are good at building AI, it does not mean you are using it ethically and responsibly,” Stirling says.&nbsp;



Among Mauritius, South Africa was also placed 59th globally on Government AI Readiness Index, Seychelles 68th, Kenya 71st, and Rwanda taking the 87th position. And although these countries are lower, Stirling elaborates on how these countries have focused on skills development which will harness the potential for AI not only in public services but in other sectors. Nations from the US, the UK to Russia, China, and Israel hold the top positions on the index, however, there are no countries in Africa in the top 20.&nbsp;



“We are lagging in technological development so we are playing catch up most of the time and essentially we are in catch up mode,” Araba Sey, Sub-Saharan Africa AI expert, commented on the Oxford Insights website.&nbsp;



Jean Lebel, President, International Development Research Centre (IDRC), added in a statement that though Ai has the potential to transform governance and public services throughout the world, there is a danger that regions, like in Africa, will be left behind. Lebel further adds that the index should encourage new collaborations and investments that could potentially assist with the barriers to government&#8217;s AI readiness and responsibilities. 



“We believe that this, in part, will help those currently lagging behind in our index to improve their AI readiness so that existing economic and technology inequality doesn&#8217;t become further entrenched and leave billions of citizens with worse quality public services,” Lebel said.

Young femal e engineer concept. GUI (Graphical User Interface).

Mauritius, South Africa & Seychelles Are In The Driver’s Seat Of AI Readiness In Africa

Forbes’ new list of&nbsp;<a href="https://www.forbes.com/sites/jilliandonfro/2019/09/17/ai-50-americas-most-promising-artificial-intelligence-companies/" target="_blank" rel="noreferrer noopener">promising artificial intelligence companies</a>&nbsp;highlights how the technology is creating real value across industries like transportation, healthcare, HR, insurance and finance.



Naturally, the founders of the honoree companies are excited about the technology’s benefits and, in their roles, spend a lot of time thinking and talking about its strengths and limitations. Here’s what they think people get wrong about artificial intelligence.



Affectiva CEO Rana el Kaliouby says she’s too often encountered the idea that AI is “evil.”



“AI—like any technology in history—is neutral,” she says. “It’s what we do with it that counts, so it’s our responsibility, as an AI ecosystem, to drive it in the right direction.”&nbsp;



Companies need to be aware of how AI could widen bounds of inequality, she adds: “Any AI that is designed to interact with humans—Affectiva’s included—must be evaluated with regards to the ethical and privacy implications of these technologies.”



Sarjoun Skaff, CTO and cofounder of Bossa Nova Robotics,&nbsp;says that the biggest misconception he encounters is that artificial intelligence is actually, well, intelligent.&nbsp;



“The truth is much more mundane,” he says. “AI is a very good pattern-matching tool. To make it work well, though, scientists need to understand the details of how it internally works and not treat it as an ‘intelligent’ black box. At the end of the day, making good use of great pattern matching still belongs to humans.”



Similarly, Aira cofounder Suman Kanuganti says that the public has “over-inflated expectations” for artificial intelligence.



“Garry Kasparov sums it up nicely: ‘We are in the beginning of MS-DOS and people think we are Windows 10,’” Kanuganti says. “AI realistically is still like a 3-year-old child at this stage. When it works, it feels magical. It does some things well, but there’s still a long way to go.”



So, no, we are nowhere close to “<a href="https://www.theverge.com/2018/11/27/18114362/ai-artificial-general-intelligence-when-achieved-martin-ford-book" target="_blank" rel="noreferrer noopener">artificial general intelligence</a>,” or AGI, where machines are actually as smart as humans.



“We’re still a long way from AI having the general intelligence of even a flea,” says David Gausebeck.



Despite the tendency to overestimate what artificial intelligence can do, the difficulty of building an effective system is often underestimated, some founders say.



“The systems you need to implement and manage machine learning in production are often much more complex than the algorithms themselves,” says Algorithmia CEO Diego Oppenheimer. “You can’t throw models at a complex business problem and expect returned value. You need to build an ecosystem to manage those models and connect their intelligence to your applications.”&nbsp;



Put another way, you can’t just “sprinkle on some artificial intelligence like a magic sauce,” says Feedzai CEO Nuno Sebastiao.



One of the most common tropes that a handful of founders brought up was the idea that artificial intelligence is primarily a job killer.



People.ai founder Oleg Rogynskyy says that AI should be seen as a creator of new opportunities instead of a destroyer of jobs.



“In a nutshell, AI does two things: It automates repetitive low-value-add work for humans (which will indeed take low-complexity jobs away), which we think of as ‘Autopilot,’&nbsp; and it guides people on how to do their work or other activities better (which makes humans more effective at what they do), which we call ‘Copilot,’” he says. “While Autopilot can take simple, repetitive and boring jobs away, Copilot is absolutely the best way to guide, train and educate humans on how to do new things.”



&#8211; By <a href="https://www.forbes.com/sites/jilliandonfro/">Jillian D&#8217;Onfro</a>, Forbes

Artificial Intelligence, technology

AI 50 Founders Say This Is What People Get Wrong About Artificial Intelligence

When Google DeepMind’s AlphaGo shockingly defeated legendary Go player Lee Sedol in 2016, the terms artificial intelligence (AI), machine learning and deep learning were propelled into the technological mainstream. 



AI is generally defined as the capacity for a computer or machine to exhibit or simulate intelligent behaviour such as&nbsp;Tesla’s self-driving carand&nbsp;Apple’s digital assistant Siri. It is a thriving field and the focus of much research and investment. Machine learning is the ability of an AI system to extract information from raw data and learn to make predictions from new data.



Deep learning combines artificial intelligence with machine learning. It is concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep learning has received much attention lately both in the consumer world and throughout the medical community.



READ MORE | <a href="https://www.forbesafrica.com/technology/2019/03/26/the-robots-are-coming-how-to-stay-relevant-and-re-skill-yourself/" target="_blank" rel="noreferrer noopener" aria-label="The Robots Are Coming: How To Stay Relevant And Re-Skill Yourself (opens in a new tab)">The Robots Are Coming: How To Stay Relevant And Re-Skill Yourself</a>



Interest in deep learning surged with the success of AlexNet, a neural network designed by Alex Krizhevsky that won the&nbsp;2012 ImageNet Large Scale Visual Recognition Challenge, an annual image classification competition.



Another relatively recent advancement is the use of graphical processing units (GPUs) to power deep learning algorithms. GPUs excel at computations (multiplications and additions) needed for deep learning applications, thereby lowering application processing time.



In our lab at the University of Saskatchewan we are doing interesting deep learning research related to healthcare applications — and as a professor of electrical and computer engineering, I lead the research team. When it comes to health care, using AI or machine learning to make diagnoses is new, and there has been exciting and promising progress.



<h2 class="wp-block-heading">Extracting blood vessels in the eye</h2>



Detecting abnormal retinal blood vessels is useful for diagnosing diabetes and heart disease. In order to provide reliable and meaningful medical interpretations, the retinal vessel must be extracted from a retinal image for reliable and meaningful interpretations. 



Although manual segmentation is possible, it is a complex, time-consuming and tedious task which requires advanced professional skills.



My research team has developed a system that can segment retinal blood vessels simply by reading a raw retinal image. It is a&nbsp;computer-aided diagnosis system that reduces the work required by eye-care specialists and ophthalmologists, and processes images 10 times faster, while retaining high accuracy.



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<h2 class="wp-block-heading">Detecting lung cancer</h2>



Computer tomography (CT) is widely used for lung cancer diagnosis. However, because visual representations of benign (non-cancerous) and malignant (cancerous) lesions in CT scans are similar, a CT scan cannot always provide a reliable diagnosis. This is true even for a thoracic radiologist with many years of experience. 



The rapid growth of&nbsp;CT scan analysis&nbsp;has generated a pressing need for advanced computational tools to assist radiologists with the screening progress.



To improve radiologists’ diagnostic performance, we have proposed a deep learning solution. Based on our research findings, our solution outperforms experienced radiologists. Moreover, using a deep learning-based solution improves diagnostic performance overall and radiologists with less experience benefit from the system the most.



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<h2 class="wp-block-heading">Limitations and challenges</h2>



Although great promise has been shown with deep learning algorithms in a variety of tasks across radiology and medicine, these systems are far from perfect. Obtaining high-quality annotated datasets will remain a challenge for deep learning training. Most computer vision research is based on natural images, but for healthcare applications, we need large annotated medical image datasets.



Another challenge from a clinical standpoint will be the time to test how well deep learning techniques perform in contrast to human radiologists.



There needs to be more collaboration between physicians and machine learning scientists. The high degree of complexity of human physiology will also be a challenge for machine learning techniques.



Another challenge is the requirements to validate a deep learning system for clinical implementation, which would likely require multi-institutional collaboration and large datasets. Finally, an efficient hardware platform is required to ensure fast processing of deep learning systems.



In the complex world of healthcare, AI tools can support human practitioners to provide faster service and more accurate diagnoses, and analyze data to identify trends or genetic information that may predispose someone to a particular disease. When saving minutes can mean saving lives, AI and machine learning may be transformative for healthcare workers and patients.



&#8211;Seokbum Ko; Professor, University of Saskatchewan



&#8211;The Conversation

Virtual reality concept.

Faster, More Accurate Diagnoses: Healthcare Applications Of AI Research

It’s a balmy 80 degrees on a mid-December day in Singapore, and something is puzzling Allen Day, a 41-year-old data scientist. Using the tools he has developed at Google, he can see a mysterious concerted usage of artificial intelligence on the blockchain for Ethereum. 



Ether is the world’s third-largest cryptocurrency (after bitcoin and XRP), and it still sports a market cap of some $11 billion despite losing 83% of its value in 2018. Peering into its blockchain—the distributed database of transactions underpinning the cryptocurrency—Day detects a “whole bunch” of “autonomous agents” moving funds around “in an automated fashion.” 



While he doesn’t yet know who has created the AI, he suspects they could be the agents of cryptocurrency exchanges trading among themselves in order to artificially inflate ether’s price. 



“It’s not really just single agents doing things on their own,” Day says from Google’s Asia-Pacific headquarters. “They’re forming with other agents to have some larger group effect.” 



Day’s official title is senior developer advocate for Google Cloud, but he describes his role as “customer zero” for the company’s cloud computing efforts. 



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As such it’s his job to anticipate demand before a product even exists, and he thinks making the blockchain more accessible is the next big thing. 



Just as Google enabled (and ultimately profited) from making the internet more usable 20 years ago, its next billions may come from shining a bright light on blockchains. If Day is successful, the world will know whether blockchain’s real usage is living up to its hype.



Danish researcher Thomas Silkjaer is using Google&#8217;s BigQuery to map publicly available information about XRP cryptocurrency addresses. The craters represent some of cryptocurrency&#8217;s largest exchanges.



Last year Day and a small team of open-source developers quietly began loading data for the entire Bitcoin and Ethereum blockchains into Google’s big-data analytics platform, BigQuery. Then, with the help of lead developer Evgeny Medvedev, he created a suite of sophisticated software to search the data.



In spite of a total lack of publicity, word of the project spread quickly among crypto-minded coders. In the past year, more than 500 projects were created using the new tools, trying to do everything from predicting the price of bitcoin to analyzing wealth disparity among ether holders. 



When it comes to cloud computing, Google is far behind Amazon and Microsoft. Last year Google pocketed an estimated $3 billion in revenue from cloud ser­vices. Amazon and Microsoft, meanwhile, generated about $27 billion and $10 billion, respectively.



Day is hoping that his project, known as Blockchain ETL (extract, transform, load), will help even the playing field. But even here Google is trying to catch up. Amazon entered blockchain in a big way in 2018 with a suite of tools for building and managing distributed ledgers. 



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Microsoft got into the space in 2015, when it released tools for Ethereum’s blockchain. It now hosts a range of services as part of its Azure Blockchain Workbench. But while Amazon and Microsoft are focusing on making it easier to build blockchain apps, Day is focusing on exposing how blockchains are actually being used, and by whom. 



“In the future, moving more economic activity on chain won’t just require a consensus level of trust,” says Day, referring to the core validating mechanism of blockchain technology. 



“It will require having some trust in knowing about who it is you’re actually interacting with.” In other words, if blockchain is to go mainstream, some of its beloved anonymity features will have to be abandoned.



A native of Placer County, California, Day got his first computer at the age of 5 and a few years later started writing simple programs. A fascination with volcanoes and dinosaurs turned his interest to life sciences, and he ultimately graduated from the University of Oregon with a dual degree in biology and Mandarin in 2000. From there he headed to UCLA to pursue a doctorate in human genetics and helped build a computer program to browse the genome.



 This Silkjaer image uses data for the XRP cryptocurrency to show the movement of funds across the entire ledger of transactions, culminating in a snapshot of funds in an actual user&#8217;s wallet.



It was at UCLA where Day began relying on distributed computing, a concept that is core to blockchains, which store their data on a large network of individual computers. In the early 2000s Day needed to analyze the massive amounts of data that make up the human genome. To solve this problem he hooked many small computers together, vastly increasing their power. 



“Distributed-systems technology has been in my tool kit for a while,” Day says.



<blockquote class="wp-block-quote">“I could see there were interesting characteristics of blockchains that could run a global supercomputer.” </blockquote>



Hired in 2016 to work in the health and bio­informatics areas of Google, Day segued to blockchains, the hottest distributed-computing effort on the planet. But the talents he had honed—sequencing genomes for infectious diseases in real time and using AI to increase rice yields—were not easily applied to decoding blockchain. 



Before Day and Medvedev released their tools, just searching a blockchain required specialized software called “block explorers,” which let users hunt only for specific transactions, each labeled with a unique tangle of 26-plus alphanumeric characters. Google’s Blockchain ETL, by contrast, lets users make more generalized searches of entire ecosystems of transactions.



To demonstrate how customers could use Blockchain ETL to make improvements to the crypto economy, Day has used his tools to examine the so-called hard fork, or an irrevocable split in a blockchain database, that created a new cryptocurrency—bitcoin cash—from bitcoin in the summer of 2017. 



This particular split was the result of a Hatfield and McCoy “war” within the bitcoin community between a group who wanted to leave bitcoin as it was and another who wanted to develop a currency that, like cash, was cheaper and faster to use for small payments. 



Using Google’s BigQuery, Day discovered that bitcoin cash, rather than increasing so-called micro-transactions, as the defecting developers claimed, was actually being hoarded among big holders of bitcoin cash.



 “I’m very interested to quantify what’s happening so that we can see where the legitimate use cases are for blockchain,” Day says. “Then we can move to the next use case and develop out what these technologies are really appropriate for.”



Day’s work is inspiring others. Tomasz Kolinko is a Warsaw-based programmer and the creator of a service that analyzes smart contracts, a feature of certain blockchains that is designed to transparently enforce contractual obligations like collateralized loans but with less reliance on third parties, like lawyers. Kolinko was frustrated with his blockchain queries.



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In December, Kolinko met Day at a hackathon in Singapore. Within a month of the meeting, Kolinko was using Google’s tools to search for a smart contract feature called a “selfdestruct,” designed to limit a contract’s life span. Using his own software in conjunction with Day’s, Kolinko took 23 seconds to search 1.2 million smart contracts—something that would have taken hours before. 



The result: Almost 700 of them had left open a selfdestruct feature that would let anyone instantly kill the smart contract, whether that person was authorized or not. “In the past you couldn’t just easily check all the contracts that were using it,” Kolinko says. “This tool is both the most scary and most inspiring I’ve ever built.” 



Day is now expanding beyond bitcoin and ethereum. Litecoin, zcash, dash, bitcoin cash, ethereum classic and dogecoin are being added to BigQuery. Independent developers are loading their own crypto data sets on Google. 



Last August, a Dutch developer named Wietse Wind uploaded the entire 400 gigabytes of transaction data from Ripple’s XRP blockchain, another popular cryptocurrency, into BigQuery. 



Wind’s data, which he updates every 15 minutes, prompted a Danish designer named Thomas Silkjaer to create a heat map of crypto flows. The resulting colorful orb reveals at a glance more than a million crypto wallets, including big ex­changes like Binance and London’s crypto debit card startup Wirex, which are neck deep in XRP transactions. 



“Google has been a bit of a sleeping giant in blockchain,” says BlockApps CEO Kieren James-Lubin, who is partnering with Google to sell enterprise blockchain apps. 



In addition to Day’s work, Google has filed numerous patents related to the blockchain, including one in 2018 to use a “lattice” of interoperating blockchains to increase security, a big deal in a world where untold millions of crypto have been stolen by hackers. 



The company is also pushing its developers to build apps on the Ethereum blockchain, and Google’s venture arm, GV, has made a number of significant investments in crypto startups. 



The giant, it seems, is waking up.



-Michael del Castillo; Forbes Staff

1130303166

Navigating Bitcoin, Ethereum, XRP: How Google Is Quietly Making Blockchains Searchable

Jaydeep Gupta, the Regional Head of Retail Banking, Africa and Middle East, Standard Chartered, on why banks need to adapt and respond to an ever-changing operating environment in Africa.

Africa’s growth prospects
There is no doubt there is significant opportunity in Africa not just in banking but across many industries. While the banking industry has faced a slight slowdown in other global markets, in Africa, this hasn’t been the case. According to a recent report by McKinsey, Africa’s banking sector is projected to grow significantly over the next five years. In fact, it’s nearly twice as profitable as the global average. Looking at retail banking, in 2017, retail bank revenue in Africa was at $35 billion and, according to McKinsey, it’s expected to increase by 8.5 % per year, reaching $53 billion by 2022.
By bringing both digitization and technological innovations into the economies, we are seeing more companies rival new segments with the progressive application of risk analytics. There is also an increased use of non-bank data for customer behavior analytics and proactiveness in looking at credit extensions over a period of time.
Digital transformation  
We have been exploring opportunities in the market to drive digital banking and, in March, we launched our first digital retail bank in Côte d&#8217;Ivoire. This marked the bank’s first digital bank in Africa and the first-of-its-kind launched.
It was the perfect opportunity to launch a fully end-to-end digital bank, with only one physical branch to test our model for future roll-out. The platform allows customers to execute all banking activities from a mobile device, including 70 banking services through the app. In addition, for the first time, the client onboarding journey has been digitised and in under 15 minutes, a client can open a new account through the app. What has also been introduced is the ability for clients to track and trace a request submitted, which is a first for Standard Chartered… We are now preparing to launch digital banks in our other markets across Africa.
Challenges in developing markets
We have faced a number of different challenges in Africa, some of the most common being the heavy reliance on cash and low-banking penetration. The most important challenge in my opinion is to stay ahead of the curve and evolve along with market demands. In today’s digital world, it’s critical that we collaborate with different partners and form alliances in order to provide clients with a convenient and superior customer experience, in line with a rapidly-evolving market.
Investing in technology
We always protect and grow our market share by utilizing our client services and capabilities which keeps us ahead of the competition. We are also putting a heavy focus on digitization, which is one of the key items on the bank’s transformation agenda. We are making strategic investments in technology to stay ahead of the curve and are constantly enhancing our products and services to make banking simpler, more convenient and secure.
A great example of this is the introduction of enhanced digital banking services, including our mobile app, SC Mobile, and Retail Workbench ‘Bank on an iPad’ platform.
<a href="https://www.forbesafrica.com/entrepreneurs/2018/04/26/banking-on-technology-new-mobile-bank-set-to-launch/" target="_blank" rel="noopener">READ MORE: Banking On Technology: New Mobile Bank Set To Launch</a>
Promoting inclusiveness
Look at multiple ways of including and empowering women in the banking industry, one of which is providing innovative and tailored products and services designed to suit the needs of women in Africa. This is especially evident in, and made possible by, the digital journey. With enhanced digital and analytic capabilities, we are able to develop solutions which are specifically designed for women, as well as students and entrepreneurs. We are also exploring ways of empowering women and providing them with the necessary tools to succeed in banking and hold leadership roles in the industry. A great example of this is our Women in Technology Incubator originally established in the United States and rolled out in Africa in 2017 with Kenya taking the lead.
<div id="attachment_37739" style="width: 310px" class="wp-caption alignright"><img aria-describedby="caption-attachment-37739" decoding="async" loading="lazy" class="size-medium wp-image-37739" src="https://www.forbesafrica.com/wp-content/uploads/2018/11/JGchosen1-300x200.jpg" alt="" width="300" height="200" srcset="https://cms.forbesafrica.com/wp-content/uploads/2018/11/JGchosen1-300x200.jpg 300w, https://cms.forbesafrica.com/wp-content/uploads/2018/11/JGchosen1-600x400.jpg 600w, https://cms.forbesafrica.com/wp-content/uploads/2018/11/JGchosen1-768x512.jpg 768w, https://cms.forbesafrica.com/wp-content/uploads/2018/11/JGchosen1-1024x682.jpg 1024w, https://cms.forbesafrica.com/wp-content/uploads/2018/11/JGchosen1.jpg 1241w" sizes="(max-width: 300px) 100vw, 300px" />Jaydeep Gupta. Photo provided.</div>
Mobile wallets
The digital banking movement in Africa started with the mobile wallet. There are over 100 million active mobile money users in Africa. The mobile wallet has improved the lives of Africans by facilitating participation and accessibility.
We are starting to witness the next generation of products being made available to customers in Africa. A great example of this is the recent widespread use of e-wallets which are beginning to pave the way to less reliance on cash in Africa. Along with less reliance, other benefits like affordability, increased security and greater convenience act as key drivers for customers.
Digital banking in Africa is especially unique because it has the potential to remedy some of the continent’s greatest economic challenges while simultaneously presenting some of the largest opportunities for digital and online banking.
More regulation 
Banking in Africa, and the world, has evolved significantly over the last 10 years. Customers are now demanding more convenient, quick, and easy services from banks. Providing online and mobile platforms is no longer a benefit, but a requirement. We have also seen regulators in Africa more heavily, and frequently, enforce regulation across the continent. This, in turn, has forced banks to adapt and respond to an ever-changing operating environment. Looking at online and mobile banking solutions specifically, banks have not necessarily replaced them, but they are definitely exploring options for updating them in line with these changes and ensuring the security of their customers. For many, this has forced them to think outside of the box.
Robotics and AI over people?
There is no doubt that robotics and Artificial Intelligence (AI) will change the people interface, but it definitely does not mean that it will replace it. If anything, leveraging advancements in technology will only complement customer services and help us be more efficient and targeted. For example, banks today are utilizing AI to analyze financial data in order to provide customers with tailor-made and fully-customizable banking solutions, designed specifically to suit customers’ individual needs.
Advanced analytics 
It’s critical that these capabilities are invested in and continue to be built on. Without proper analytics, companies will struggle to understand consumer behavior. To provide customers with the best possible service, you must understand where they are coming from. Therefore, it is very important that solid collection strategies and procedures are put in place.
<h2>Africa: hotbed of innovation</h2>
<ul>
<li>According to a recent report by McKinsey, over 40% of African banking users prefer to use digital channels for their financial needs. This is no surprise and is indicative of the digital shift we are witnessing across sub-Saharan Africa.</li>
<li>The African banking sector has been described as a ‘hotbed’ of innovation. As Africa begins to enter an exciting era of digitization, innovation will play a key role in unlocking the future potential of banking.</li>
</ul>

Mobile/Contactless Payment

Why Digital Banking Is Unique In Africa

Automation in the workplace is already well underway. The McKinsey Global Institute estimates that up to 50% of workplace tasks could be <a href="http://www.mckinsey.com/global-themes/digital-disruption/harnessing-automation-for-a-future-that-works" target="_blank" rel="noopener">fully automated by</a><a href="http://www.mckinsey.com/global-themes/digital-disruption/harnessing-automation-for-a-future-that-works"> 2055</a>. For employees, this can be a daunting concept, as increased automation in professional settings could lead to job loss on a mass scale.
Yet, automation in the workplace might not be all doom and gloom, depending on how we look at it. Rather than viewing automation through the lens of robots replacing humans, it could be more productive and beneficial to see view increased automation as a great potential for collaboration and growth. In fact, automation could conceivably <a href="https://www.forbes.com/sites/forbeshumanresourcescouncil/2017/05/02/the-impact-of-automation-on-the-independent-workforce/#1f6f901575c5" target="_blank" rel="noopener">boost the independent workforce</a>, making the professional landscape more human centric than ever.
Here are some key ways that automation can help us nurture the human side of the workplace, making room for a future that is both efficient and innovative.
More hours in the day
Managing daily tasks can be incredibly consuming, and as many businesses know, ticking boxes and making sure everything is in order can take away from time spent working together. Implementing automation in the workplace can give you more wiggle room, as it can be an effective way to <a href="https://frontapp.com/blog/2017/02/22/how-to-automate-customer-support-without-losing-authenticity/" target="_blank" rel="noopener">optimize workflow management </a>by helping teams remain on top of tasks and automatically organizing important information. In addition to making sure things go smoothly internally, automation can also help manage and even improve your customer relations. Not only can a chatbot be available 24/7, it can also learn from the information given to them by your consumers, thus becoming more personalized and able to meet specific customer needs in the future.
<a href="http://www.forbesafrica.com/entreprenuers/2018/01/26/meet-young-robotics-entrepreneur-got-dream-deal-apple/" target="_blank" rel="noopener">READ MORE: Meet The Young Robotics Entrepreneur Who Got A Dream Deal With Apple</a>
Shift focus to creativity
Having the ability to think outside the box in the workplace is a key part of encouraging growth and innovation in any work environment. In a similar way that artificial intelligence can grant us more room to focus on the people that we work with, it can also grant businesses more time to focus on creative output. This doesn&#8217;t just mean giving more time to creative projects, but learning how to <a href="https://blog.hubspot.com/agency/company-policy-improve-creative" target="_blank" rel="noopener">creatively approach </a>work to make your company culture more human centric. By automating certain tasks such as customers relations management and accounting, those hours that would have been spent going over paperwork can now be transformed into team outings and coming up with new ideas for growth.
Better understand how technology can benefit consumers
A combination of data driven research and emerging market trends have taught businesses how to interact with their consumers in a way that will ensure they have their personal needs met. Learning how to implement strategic data analysis is an effective way for your business to get to know their customer. In addition to better understanding consumer needs, understanding how to make the most of data analysis, like how to use it to predict market trends.
<a href="http://www.forbesafrica.com/focus/2018/01/25/four-skills-make-robot-recession-proof/" target="_blank" rel="noopener">READ MORE: Four Skills To Make Yourself Robot And Recession Proof</a>
While automation in the workplace might still seem a bit like science fiction, advancements in AI have proven that we are not far from having an intelligent workplace in the near future. It’s important that businesses find ways to encourage their employees to work in tandem with artificial intelligence. By understanding how to strategically implement artificial intelligence in the workplace based on your human needs, it’s an ideal way to boost innovation and company morale in the future.

Is Automation All Doom And Gloom?

Artificial intelligence (AI) has been at the forefront of innovators minds for decades and as the technology makes breakthroughs, the public is becoming more aware of its implications.
The scientist, innovators, and entrepreneurs who dedicate their lives to the pursuit of high-level artificial intelligence are pushing the boundaries of what can be accomplished every day.
We are seeing the direct result of their breakthroughs in the forms of automation and autopilot. Through this pursuit one thing becomes clear: AI will change our lives dramatically in the coming years. That is why it is not only fascinating, but also imperative that we stay abreast of the current climate in the world of AI. These innovations are doing amazing things in the world of AI that could affect the planet for decades to come.
<a href="http://www.forbesafrica.com/focus/2018/01/25/four-skills-make-robot-recession-proof/" target="_blank" rel="noopener">READ MORE: Four Skills To Make Yourself Robot And Recession Proof</a>
<ol>
<li> Self-Driving Cars</li>
</ol>
The most popular innovation, as well as the most exciting, is the advent of autonomous vehicles. For the last 50 years people have imagined a world where driving would no longer be necessary to enjoy personal and private transportation.
Driving in the United States has been a leading cause of death among youth for a number of years. One of the primary goals of autonomous vehicles is to reduce the number of casualties, while simultaneously increasing the efficacy of transportation services across the globe. With safety and value in mind, autonomous vehicles ranks as one of the most ambitious and exciting innovations to come.
<a href="http://www.forbesafrica.com/technology/2017/07/21/push-self-driving-car-rules-overlooks-lack-federal-expertise-ai-tech/" target="_blank" rel="noopener">READ MORE: Push For Self-Driving Car Rules Overlooks Lack Of Federal Expertise In AI Tech</a>
<ol start="2">
<li> Natural Language Processing</li>
</ol>
A main goal of AI in the future is to create machines that can reason and write with the lexical skill of a human being. Natural Language Processing (NLP) is the software that will hopefully achieve one such future that’s being developed primarily in <a href="https://www.python.org/" target="_blank" rel="noopener">Python</a>.
Companies, like Attivio and Alphabet, are working constantly to replicate human thought and speech patterns. The goal of this is to ultimately create machines that can communicate with us, and us to them. This would fabricate a new type of machine with unlimited possibilities of growth. This could lead to machines replacing lawyers, doctors, or even therapists. The projected future of this technology is endless. This makes NLP one of the most intriguing innovations to watch.
<ol start="3">
<li> Virtual Agents</li>
</ol>
Virtual Agents (VA) have a simple but profound goal: to create AI that can guide us through customer service effectively and quickly. As of now the AI is mostly used for automated chat resources but with enough progress and innovation, businesses hope to replicate the experience of human customer service. Fused with the efficiency of a machine, this would also eliminate the wait time problem that we all have been through.
Companies like Apple and Amazon have put themselves on the cutting edge of this technology with the view that great customer service would help their brands exponentially. This technology, however, is closer than we imagined due to programs like deep learning in which a program can watch and learn from humans interacting. This innovation will most likely be affecting us, and easing tension, very soon.
<a href="http://www.forbesafrica.com/technology/2017/09/13/apple-not-google-facebook-will-define-future-augmented-reality/" target="_blank" rel="noopener">READ MORE: Apple, Not Google Or Facebook, Will Define The Future Of Augmented Reality</a>
<ol start="4">
<li> Robotic Process Automation</li>
</ol>
One technology that businesses are particularly keen on is the Robotic Processing Automation (RPA) AI software. RPA is simple in its conception and seeks to automate more complex human tasks, like filing reports or reading mail. This, however, has some obvious concerns for workers in the middle classes as they may get replaced, but the impact of such a comprehensive technology could change the nature of work forever.
The company Automation Anywhere is one such business that is seeking to change work for the better. This could alleviate issues that arise from companies with too few employees or too tight a budget to hire who they need. Still in larval stages, this AI platform won’t hit full stride just yet, but it is on its way.
<a href="http://www.forbesafrica.com/entreprenuers/2017/09/11/battling-giants/" target="_blank" rel="noopener">READ MORE: Battling Giants</a>
<ol start="5">
<li> Machine Learning</li>
</ol>
Machine learning is the process whereby a program can analyze a system and replicate its functions automatically. This could consolidate machine usage and create more efficient algorithms. It helps with predictions and forecasting by synthesizing data and outputting a report which streamlines the data gathering process.
Amazon and Fractal Analytics are in full swing on developing this software as it could help businesses operate at lower cost with higher efficiency. For all intents and purposes, machine learning is here and is advancing at an astronomical rate. Keep your eyes peeled because your company may be involved in machine learning before you know it.
Whether we like it or not AI is here. With it comes fears and hopes, but most importantly change. Some of these technologies are right around the corner, some are far off. Keep your eyes on the future.

5 AI Innovations You Need To Be Aware Of

In the summer of 2013, as Matthew Zeiler was close to finishing a Ph.D. in artificial intelligence at New York University, he seemed to have every tech giant in the palm of his hand. Zeiler had left an internship with a Google AI group a few weeks earlier when he got a call from an unknown number while he was running along the Hudson River. It was Alan Eustace, then a senior vice president of engineering at Google, who had heard about Zeiler&#8217;s AI chops. Eustace wanted Zeiler to join permanently. To entice him, Eustace told him he would make an offer that was among the highest Google had ever made to a new graduate, Zeiler recalls. Zeiler won&#8217;t say how much he was offered, and Google declined to comment. But offers for top recruits with specific expertise can add up to several millions of dollars over four years, according to people with knowledge of the matter. Regardless, Google&#8217;s offer kicked off a bidding war for Zeiler and his know-how in deep learning, the vaunted branch of AI that&#8217;s driving major breakthroughs in computing.
Within days, Zeiler received a bigger offer from Microsoft, which Google promptly matched. Apple also wanted to chat, and when Zeiler flew out to Silicon Valley, Mark Zuckerberg personally sought to persuade him to join a new AI research group at Facebook. Zeiler respectfully turned them all down, deciding instead to start a company with an audacious goal: to compete with the giants that were courting him. &#8220;It was a crazy period,&#8221; Zeiler remembers. &#8220;I had this low-risk opportunity of joining a tech giant versus doing my own startup.&#8221;
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Zeiler says he knew that some of his algorithms worked better than Google&#8217;s on certain AI problems. &#8220;I knew I had to follow my gut,&#8221; he says.
Four years later, Zeiler&#8217;s New York City-based startup, Clarifai, is widely seen as one of the most promising in the crowded, buzzy field of machine learning. Clarifai offers image- and video-recognition tools for developers that rival those from Google, Microsoft and others. Much as Stripe and Twilio make it easy for programmers to tap into payments and communications capabilities, Clarifai gives its customers access to cutting-edge AI techniques that would cost millions to replicate. Companies like Unilever, BuzzFeed, Ubisoft and Staples U.K., as well as makers of medical devices and drones, use Clarifai to automatically analyze millions of images and videos. One of the company&#8217;s 100 or so customers, i-Nside, makes a smartphone accessory for imaging the inside of an eardrum and diagnosing ear diseases. Revenue, while still small, is expected to reach $10 million as early as next year, according to people close to the company.
That Clarifai has made it this far is, in and of itself, remarkable. In the past few years, AI &#8211; in particular a form of it called deep learning or deep neural networks &#8211; has emerged as the Next Big Thing in tech. Deep-learning techniques work loosely like the brain, with layers of &#8220;neurons&#8221; connected with &#8220;synapses.&#8221; The techniques are leading to substantial breakthroughs in areas like image and speech recognition, which in turn are ushering in advances in everything from medicine to self-driving cars to robotics.
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But there&#8217;s a problem: Amid the scramble for talent, the richest companies in tech have consumed entire university departments and acquired just about every AI startup they could get their hands on. Google has been the hungriest, with at least 11 AI-related acquisitions, spending upwards of $1 billion for just two of those, DeepMind and api.ai. Nearly all the upstarts that competed with Clarifai have been bought: Amazon acquired Orbeus; Salesforce got MetaMind; IBM snapped up AlchemyAPI. When it comes to image recognition, Clarifai is perhaps the only one left that can compete with Amazon, Google, IBM and Microsoft, all of which offer AI image-recognition tools to their cloud-computing customers. Clarifai has already rebuffed several multi-million-dollar acquisition offers, according to an early employee. Zeiler says he is determined to keep the company independent.
Clarifai has none of the might or reach of its rivals, but Zeiler insists, convincingly, that playing Switzerland in a global AI war is a valuable asset. Many large companies that want to incorporate AI into their products are fearful of handing over their data to giants like Google and Amazon. Photobucket is a case in point. After assessing competing tools from Amazon, Google and IBM, the image- and video-hosting service became one of Clarifai&#8217;s largest customers in terms of image volume. &#8220;Any time you&#8217;re dealing with Google, you have to wonder if they&#8217;re taking your data and training their own system,&#8221; says Mike Knowles, senior infrastructure developer at Photobucket. With its Photos app, Google competes with Photobucket. Zeiler says many other potential customers are at risk of colliding with the ever-expanding ambitions of tech&#8217;s biggest companies. &#8220;They open new divisions that compete with their customers,&#8221; Zeiler says. &#8220;That&#8217;s what we don&#8217;t do.&#8221;
At 30, Zeiler, who grew up in Beausejour, a small town in Canada some 40 miles northeast of Winnipeg, seems an unlikely challenger to tech&#8217;s powerhouses. With slicked-back hair that he cuts only a couple of times a year, he retains the disheveled air of a college student.
But Zeiler&#8217;s obsession with AI put him on a path to be mentored by some of the field&#8217;s biggest luminaries. Oddly enough, his interest in the field started with a video of a flickering flame that he saw while an undergraduate at the University of Toronto. The video, shown to him by a grad student, looked startlingly real, yet it was generated by a computer using an AI technique. Zeiler had just learned the basics of computer programming but hadn&#8217;t taken to it. The flame represented something different. No human had explicitly programmed it to move around in predetermined ways. Instead, a computer had been fed video data, deduced a pattern and generated the video on its own. &#8220;I was completely blown away,&#8221; Zeiler says. &#8220;It was a whole new way to get computers to do what you wanted. I had to learn more.&#8221;
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Graham Taylor, the Ph.D. candidate who had shown him the video, brought Zeiler into a research lab that was run by Geoffrey Hinton, widely considered one of the godfathers of neural networks. Taylor liked the ambitious yet amiable Zeiler. &#8220;He was smart but wasn&#8217;t a jerk,&#8221; Taylor says. In Hinton&#8217;s lab Zeiler worked on using AI techniques to track pigeons&#8217; mating rituals, resulting in his first paper, &#8220;Learning Pigeon Behaviour Using Binary Latent Variables.&#8221; He graduated at the top of his class.
Zeiler then headed to NYU for a Ph.D., following Taylor, who was a postdoctoral student there. Taylor worked under Yann LeCun, another pioneer in deep learning, who now heads Facebook&#8217;s AI efforts. Eventually, Zeiler did two internships at Google and worked for Jeff Dean, the head of a then-new deep-learning research group called Google Brain. Hinton, who now works at Google and retains a position at the university, was part of that 20-person AI skunkworks. (Google Brain has since grown into one of the most high-profile and vital groups within Google.)
Zeiler founded Clarifai in November 2013 after his second internship, just as he was finishing his Ph.D. The company got off to an auspicious start. Zeiler tested his image-recognition algorithms in a highly regarded contest called ImageNet. The 2012 ImageNet had shaken the AI world when a team from Hinton&#8217;s lab in Toronto, using deep-learning techniques, cracked a huge barrier in accuracy: Its error rate was 15%, far better than the 25% attained with earlier AI approaches. In 2013, Zeiler beat out the competition with an error rate of just 12%.
For the next few months, Zeiler worked alone, pushing the limits of his neural networks and rewriting the code to turn it into a commercially viable product. He installed four servers in his apartment to crawl the Web for images to train his algorithms. At one point, his apartment got so hot that he had to leave his windows open in the middle of winter. By April 2014, Zeiler hired a second employee, and the two moved the servers to a New Jersey data center, where Clarifai continues to expand. In October 2014, he made the service available to developers. His first customer was a wedding lifestyle website called Style Me Pretty, which uses Clarifai to identify and categorize thousands of user-uploaded pictures and serves ads based on what&#8217;s in an image.
In 2015, Clarifai landed its first sizable investment: a $10 million round led by Union Square Ventures. The corporate coinvestors, who clearly understood the potential of what Zeiler was building, included Qualcomm, AI chip specialist Nvidia and, interestingly, Google&#8217;s venture arm. The following year, in a round led by Menlo Ventures, Clarifai raised another $30 million, at a valuation of $120 million, according to PitchBook. &#8220;Tech giants are working on similar products, but they don&#8217;t wake up every day living and dying on building the best image-recognition service,&#8221; Menlo partner Matt Murphy says. Clarifai now has 55 employees, including 10 dedicated to digging through the latest AI research so the company can stay current. Last year, it hired a veteran sales executive from Google&#8217;s enterprise unit as its chief customer officer.
A recent study by the consulting firm CapTech shows Clarifai remains competitive with, and in some cases outperforms, tech giants like Amazon, Google and Microsoft in image recognition. But finding and keeping AI talent to maintain that position&#8211;let alone expand into new areas like audio recognition and beyond&#8211;won&#8217;t be easy. In February, Clarifai scored a longtime Google AI researcher, Andrea Frome, as its head of research, but she abruptly departed after only four months. Frome declined to speak about her departure, and Zeiler says it was the result of differing priorities. Access to data&#8211;lots of it&#8211;to &#8220;train&#8221; algorithms is also an area where Clarifai is likely to find itself at a permanent disadvantage compared to its much-larger rivals.
Clarifai&#8217;s latest tool trains AI models on smartphones, not in the cloud, where most AI systems do the bulk of their computing. On a recent day, in a San Francisco hotel lobby, Zeiler pulls out his cracked iPhone 6. As he moves the camera, the phone identifies all the objects around it&#8211;chairs, a fireplace, people, cars, as well as a MacBook that Zeiler had just trained it to recognize. It&#8217;s a tantalizing demonstration of the potential for deep learning as it moves into the most important device in people&#8217;s lives. &#8220;We&#8217;re only seeing the tip of the iceberg of what these systems will be able to do,&#8221; Zeiler says. &#8211; Written by <a href="https://www.forbes.com/sites/aarontilley/" target="_blank" rel="noopener" data-ga-track="contrib block byline">Aaron Tilley</a>, FORBES STAFF

Battling Giants

United States-based speaker, author and futurist Jack Uldrich was in Johannesburg recently for the 2017 Future of Health Summit held by FORBES AFRICA and Philips. Amid all the talk about AI eye robots, virtual nurses, the potential for blockchain to transform electronic health records, and even the inception of ‘uber-ambulances’ as one doctor in the audience proposed, Uldrich spoke some more to FORBES AFRICA about what the future holds for technology, healthcare and life:
Technology companies will lead the future of healthcare. Tell us more. 
From my perspective, it’s all about data. Amazon, Google, even Facebook, know so much about us, they are going to figure out how to use data to keep us healthy, and they are going to figure out how to make money keeping us healthy, using data.
Which means people will probably live a lot longer – how is technology going to aid longevity? 
Longevity is a wonderful problem to have but it is going to impose serious societal pressures on government. In the West, our social security systems are geared for a life expectancy of below 80, but we are going to be living to a 90 and potentially a 100. Africa might not be that high yet, but society is going to have to deal with this; public policy-makers will have to deal with thinking about this issue yesterday. 
What will be interesting is we might begin to rewrite social contracts, in the sense that if we live to be a 100, maybe it will make sense for young parents to spend a lot more time, when they are in their 2os and 30s, understanding that they will be able to work full-time from 40 to 90 years of age for example. There will be a lot of different potential solutions to this and we have to get creative.
8,000 days of the internet; what will another 8,000 days mean for the internet? 
This is where people have to understand exponential growth. Without a doubt, the internet has been transformative, but we are still in the early days of this revolution, and we are next going to move to the Internet of Things, and the transition is as though we are looking in the internet. Information and data is going to be all around us and we are going to be able to leverage that data to understand our health better, and to use that information to engage in healthier behaviour that keeps us healthy.
<div id="attachment_32467" style="width: 253px" class="wp-caption alignleft"><img aria-describedby="caption-attachment-32467" decoding="async" loading="lazy" class=" wp-image-32467" src="http://www.forbesafrica.com/wp-content/uploads/2017/06/Jack-Uldrich-683x1024.jpg" alt="" width="243" height="364" />Futurist Jack Uldrich</div>
What next after the Fourth Industrial Revolution? 
Industry 4.0 and the Internet of Things are, in my mind, interchangeable terms, but we are just at the beginning. Today, 20 billion physical objects are connected to the internet, by 2020, an estimated 50 billion, and by 2030, a trillion. But that means our wearable devices, our electronic health records, our rooms, are all going to be connected, and we are going to be able to leverage that data to do, think and act differently about our health in the very near future.
Will robots ever replace us fully? 
I don’t think they will fully replace us. The way I think about technology and humans is this: we just celebrated the 20th anniversary of a computer beating the world’s best chess player. Computers have gotten significantly smarter in the last 20 years but there is still one thing that can still beat the world’s most powerful computer in chess, and that’s an average chess player using an average computer. Human experience, intuition, plus technology take us to a higher level and when I think about the future of physicians, other healthcare professionals, those that thrive are going to be those that leverage technology and let technology do what it does really well, and then we will focus on the more human aspect. And for healthcare, this is really important. People don’t just want the best treatment; they want to be heard, they want to understand their options for disease and treatment. Could a computer do that? Yes, but I think that humans for the foreseeable future will be significantly better.
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Are virtual nurses considered far more effective than real nurses?
<blockquote>If I have any advice about the future, it is to learn how to embrace ambiguity.</blockquote>
We frequently like to think the world is going to happen one way or another way. And virtual nurses are a wonderful example. In the West, many patients prefer being checked out by virtual nurses, and that’s because they don’t feel judged, they can ask as many questions, and because of that, they are getting better information. So let’s allow the virtual nurses to check people out of the hospital, which then frees up the nurse to spend a lot more time on the human aspects and taking care of patients. The human touch is really important to the recovery process and if we can have more healthcare professionals engaging in real human activities, that’s a wonderful thing.
What about the new <a href="https://www.wired.com/2017/06/googles-ai-eye-doctor-gets-ready-go-work-india/" target="_blank" rel="noopener noreferrer">AI eye doctor</a> that Google has come up with, and putting to work in India? 
Google has collected millions of images of the eyes of patients with diabetes, and from those images, we can now engage in preventative actions that will prevent potentially hundreds of thousands of people from going blind. They are going from India to Africa to the entire world.
Your take on <a href="https://www.forbes.com/sites/joannmuller/2017/06/13/in-the-race-toward-self-driving-cars-gm-has-something-apple-and-googles-waymo-lack-a-car/#7b9c2196764f" target="_blank" rel="noopener noreferrer">self-driving cars</a>…? 
In every city around the world, the average automobile sits idle 22 hours a day so that is a waste of capital resource. In a world of driverless vehicles, we are going to be able to have fewer vehicles, but we are still going to be able to get people from their destinations, and what this means is people will start trading ownership of cars for access. But these vehicles will be clean, safe and cost a fraction of what we spend on transportation. 
In the future, transportation is going to be thought of as a service much like electricity, in the sense that you will just pay for the transportation that you need, for the kilometers you drive or are driven.
Will the seat configurations change in a driverless vehicle; do we still sit in the front? 
If you own a driverless vehicle, you can sit wherever you like, and for the near future, you will sit where you traditionally sit, but as you grow more comfortable with it, you might sit in the back and then we are going to start redesigning, and instead of all of the seats facing forward, we might actually face one another and have conversations with our peers, but here’s where it gets interesting. People will still own vehicles, but because of blockchain technology, they might be able to share that vehicle with strangers and rent out that vehicle when they are not using it, and make money off of that vehicle.
Objects that will alert us to heart attacks before it happens; is this technology available now? 
The idea of a heart attack ringtone is being worked on. There is a doctor in San Diego named <a href="http://www.mddionline.com/article/ringtone-tell-you-you-are-having-heart-attack-thats-no-joke-heart-doc-tells-stephen-colbert" target="_blank" rel="noopener noreferrer">Dr Eric Topol</a> who has spoken about this idea for a few years. Computer chips are becoming small enough that we can already put them in the human body, and those computer chips have sensors and we can now detect the proteins that float around in our bodies that are indicative that you are about to have a heart attack. So that computer chip will be able to alert you and your healthcare provider even before a heart attack happens, and you will be able to take preventative action. 
It’s not science fiction, it is science fact.
What is the zaniest thing you have heard about the future, say five years from now? 
Within the next five years, the possibility that we are going to be able to begin 3D-printing human organs is a possibility. In 10 years, the odds increase even more. But in five years, there is a chance it may happen. What is crazy is today a car can be a 100 years old, but you can keep it operating because you just get it a new motor or a new pump, but we might begin thinking about the human body that way. Your part might go, but we might just print you a new one using your stem cells that we know will work in your body. But we need to begin to think that just because we can, is this necessarily a wise path that we can take, and this is where we need philosophers, citizens and politicians, etc to engage.
Do we really need to live that long? Is it going to be economically viable? 
At this stage, what I tell people is let us give up on answers, let us just stick to questions for the time being. We need to ask better questions about the future, and this idea of longevity and 3D-printing human body parts… we need to ask a lot of questions about these and many other issues.
Where is Africa in terms of advances in tech? 
Africa is behind today but the potential to rapidly catch up to the US is a real possibility because virtually every technology is not only getting better, it is getting more affordable. In many cases, counter intuitively, the infrastructure of the West and our powerful political organizations might actually slow down some innovation. And I think the opportunity for Africa to not only catch up but to also potentially leapfrog in some areas is a real possibility, and I think the West should have some humility and begin looking to Africa for innovations that I think are really going to surprise us, just as the way <a href="https://www.cnbcafrica.com/news/east-africa/2017/01/04/mpesa-economic-impact-on-kenya/" target="_blank" rel="noopener noreferrer">mobile payment took off in Kenya</a>. I think the West might need to catch up to Africa in some cases as well.
<a href="http://www.forbesafrica.com/focus/2016/07/01/industrialization-ditched-tech/" target="_blank" rel="noopener noreferrer">Industrialization Ditched For Tech</a>

Tag: AI

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