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The case for and against democratizing artificial intelligence

Democratizing artificial intelligence means making it accessible to all. This involves providing access to AI tools and data required to build AI systems. Democratizing AI will thus lead to better innovations, more efficient AI systems, and even better engagement. In the current state of affairs, cash-heavy big firms and tech-savvy start-ups are the major users of AI.

The aim of democratizing AI is to make it available across a broad spectrum of entities such as government agencies as was demonstrated during contact tracing for Covid and prioritizing groups for Covid vaccine distribution, and even extending it to smaller entities or even individuals. A good example of democratization of AI is that of Makoto Koike of Japan. In 2015, Koike, while helping his farmer parents to sort cucumbers at their farm, used Google’s Tensorflow, an open source library. A ‘deep-learning’ algorithm was able to help him classify cucumbers based on photos. The results were far from quite accurate due to the small dataset. The images fed to the system were also of a low-resolution. But his story went viral on Youtube as it told a tale of the hitherto unharnessed potential of AI use across human tasks.

The problem with democratizing, however, arises due to the lack of knowhow on coding, statistics, data analysis etc., simply due to an inadequate number of personnel equipped with such knowledge. Even if they are, the cost of hiring them is exorbitant. So, tech giants such as Google, Amazon et al offer easy-to-use cloud technology and easy-to-use AI tools such as virtual assistants and even Natural Language Processing, which can be easily incorporated into apps sans any knowledge of making machine learning (ML) models. While these are used to boost businesses, such democratization, though affordable, can be dangerous and highly susceptible to bias.

Cheaper cloud tech also means using the services of newly minted AI specialists with less experience and knowhow, leading to use of such data which is not advanced in its quality. The use of poor quality data may unleash negative effects on the firm or user, and may not be detected until the point-of-no-return is reached. This is where the role of specialized data scientists becomes imperative to oversee smooth functioning of AI.

It is also critical to ensure that people who use such user-friendly AI tools should have some knowledge about the functionality of such tools and not simply the know-how to operate them.

Simply extending AI tools without in-depth knowledge equals the most common form of untrained danger such as asking a dental surgeon to operate on a limb. Similarly in the field of AI, the lack of interpreting data in the correct manner can be just as hazardous.

Another common issue faced while implementing AI democratically is the limitation of its knowledge to the top leadership. Unless employees at the bottom of the ladder also know how to use AI, data transparency cannot be achieved. A fitting example is that of Airbnb, who are implementing data transparency by giving access to employees and thus helping them in decision-making.

Data is now available to everyone and we at Infinite Analytics are committed to make gold out of all your data. Our platforms and tools are home made and global. Moreover, Democratizing AI cannot happen in an instant. It will work only with in-depth and wider knowledge and commitment to sharing the complete knowhow instead of just the upper realms of data-science. You can follow our posts and insights at Infinite Analytics (substack.com)

Video editing and film-making

If you have ever been in the business of video editing, especially when a famous celebrity was involved, and no matter how much you try to salvage the video, you cannot; deepfakes will help with that clip and your video will be made uninterrupted. Earlier, movie directors would spend millions on creating the perfect location for their movies. This is now achievable with a far lesser sum using deepfake and AI. Deepfakes can be real game-changers in film-making. Imagine new movies starring Charlie Chaplin opening to full houses. This, ofcourse, after all the copyright issues are taken into consideration..

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wasim basir

marketing, board member

It’s most obvious in the digital media space, from click buys to personalized web experiences. For marketing, the AI journey has just kick-started, while in the tech sector it has been applied for a while now. We are still at an early stage where inroads are being made into AI content via chatbots and even some explanatory content creation but what will make anyone jump up and embrace it is when we will start seeing a lot of mainstream content being created by AI.

rich arnold

board member

Prior to joining Infinite Analytics, Richard served as the CFO of CrowdFlower, COO and CFO of Phoenix Technologies, as a member of the board of directors and chairman of the Audit Committee at Intellisync, and previously as CFO and executive vice president strategy and corporate development at Charles Schwab.

pravin gandhi

board member

Pravin Gandhi has over 50 years of entrepreneurial operational and investing experience in the IT industry in India. He was a founding partner of the first early stage fund India - INFINITY. Subsequently a founding partner in Seedfund I & II. With over 18 years of investing experience, he is extensively well networked in investment and entrepreneurial scene and is an active early stage angel investor in tech & impact space. Pravin holds a BS in Industrial Engineering from Cornell University, and serves on the board of several private corporations in India. He is on the board of SINE, IIT Mumbai Incubator.

Purushotham Botla

co-founder & cto

Puru has his Masters in Engineering and Management from MIT. Prior to MIT, he worked with Fidelity Investments building electronic trading products and high volume market data processing applications. He has completed his BE from VJTI, Mumbai.

deb-roy

Deb Roy

Executive Director, MIT Media Lab

Deb Roy is Professor of Media Arts and Sciences at MIT where he directs the MIT Center for Constructive Communication, and a Visiting Professor at Harvard Law School. He leads research in applied machine learning and human-machine interaction with applications in designing systems for learning and constructive dialogue, and for mapping and analyzing large scale media ecosystems. Deb is also co-founder and Chair of Cortico, a nonprofit social technology company that develops and operates the Local Voices Network to surface underheard voices and bridge divides.

Roy served as Executive Director of the MIT Media Lab from 2019-2021. He was co-founder and CEO of Bluefin Labs, a media analytics company that analyzed the interactions between television and social media at scale. Bluefin was acquired by Twitter in 2013, Twitter’s largest acquisition of the time. From 2013-2017 Roy served as Twitter’s Chief Media Scientist.

Erik Brynjolfsson

Board Member

Erik Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI), and Director of the Stanford Digital Economy Lab. He also is the Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR), Professor by Courtesy at the Stanford Graduate School of Business and Stanford Department of Economics, and a Research Associate at the National Bureau of Economic Research (NBER).

Akash Bhatia

Co-Founder and CEO

Akash co-founded IA while studying for his MBA from MIT. Prior to MIT Sloan, he co-founded Zoonga. Before this, Akash was an engineer with Oracle in Silicon Valley. He has completed his M.S from University of Cincinnati and B.E from the College of Engineering, Pune.