3-2-1 on AI: April Edition

May 2, 2022

Our weekly 3-2-1 on AI newsletter features some of our favorite resources and discussions on the latest AI industry trends, data and thought-provoking quotes, and quick updates on what we’ve been working on.

Check out a summary of last month’s links below and sign up to receive our weekly newsletter straight to your inbox here.

Top picks from the AI Community

  • Andrew Ng is among the most prominent figures in AI, as founder of LandingAI and DeepLearning.AI, co-chairman and cofounder of Coursera, and adjunct professor at Stanford University. In this interview, he discusses the move towards "data-centric AI" and his predictions for the next 10 years in AI. (Article)

  • In AI, the “alignment problem” refers to the challenges caused by the fact that machines simply do not have the same values as us. In his latest book, Brian Christian discusses the dangers of not aligning AI with human values, highlighting real-world examples, and explores where we are when it comes to solving them. (Interview)
  • Bias is neither new nor unique to AI and it is not possible to achieve zero risk of bias in an AI system. In a new report, the National Institute of Standards and Technology (NIST) discusses methods for increasing assurance, governance, and practice improvements for identifying, understanding, measuring, managing, and reducing bias. (Report)
  • What makes a company “future ready”? An analysis of top companies across four sectors, and what leading companies have been doing differently when it comes to exploring new technologies. (Article)
  • Even if you’re sold on the idea that the future of work is human-machine partnership, how exactly does it work best? Who decides who does what? And how can humans learn to trust AI? New research offers some answers. (Article)
  • AI and machine learning requires huge amounts of processing capacity and data storage, making the cloud the preferred option but also raising the specter of a few cloud giants dominating AI applications and platforms. Should we worry that tech giants take control of the AI narrative and reduce choices for enterprises? Not necessarily, but there are some caveats, according to AI experts.  (Article)
  • The Biden administration took its first substantive step to shape US policy on AI, as Europe, China, and other countries leap ahead with their own rules. Twenty-seven people from across the private sector and academia will make up the 'National Artificial Intelligence Advisory Committee'. (Article)
  • AI combined with a human-centric approach to marketing might seem like a contrarian model. But the truth is that machine learning, AI and automation are vital for brands today to transform data into empathetic, customer-centric experiences. (Article)
  • In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. Researchers at MIT and IBM Research have created a new method, called 'Shared Interest', that enables a user to aggregate, sort, and rank individual explanations to rapidly analyze a ML model’s behavior, incorporating quantifiable metrics that compare how well a model’s reasoning matches that of a human. (Article)
  • The EU has agreed on another ambitious piece of legislation to police the online world. The Digital Services Act, or DSA, will force tech companies to take greater responsibility for content that appears on their platforms – including explaining how their algorithms work, removing illegal content and goods more quickly, and taking stricter action on the spread of misinformation. (Article)
  • Technology is changing very rapidly, and the changes are accelerating. However, changing an organization — how it thinks and behaves — is still hard and slow. A look at how we can visualize the interplay between these two dynamics. (Article)
  • There's a growing realization that AI needs to reside on purpose-built infrastructure if it is to bring real value to the business model. In fact, lack of proper infrastructure was cited as one of the primary drivers for failed AI projects, which continues to stymie development in more than two-thirds of organizations. (Article)

AI Quotes We Love

“70% of U.S. workers want AI to be a part of their jobs.” – Gartner
“Dig into every industry, and you'll find AI changing the nature of work.” — Daniela Rus
“One of the big roles of leaders [during a digital transformation] is to create a safe, supporting environment where people are able to learn. They can't [learn] if they're constantly feeling like their job is in jeopardy or their reputation is in some way vulnerable.”  — Kristine Dery
“By 2024, 75% of organizations will shift from piloting to operationalizing AI.” — Gartner
“Finding a way to generate value from data and AI won’t happen without intentionality. It takes a combination of the right foundation, the right people, and strategic decisions to put data and AI at the center of everything you do.” — Joseph Depa
“AI technologies could deliver up to $1 trillion of additional value annually for global banking.” — McKinsey
“The victory represents a new milestone for AI because in bridge players work with incomplete information and must react to the behaviour of several other players – a scenario far closer to human decision-making.” – Laura Spinney on AI beating eight world champions at bridge
“It is the people and process that make-or-break digital transformation. And training the humans is as important as training the AI.” — Sanjay Srivastava

... and an update on Apres

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