Today, deep learning is the talk of the town. There is no shortage of media coverage, papers, books, and events on deep learning. Yet deep learning is not new. Its roots go back almost to the early days of artificial intelligence and computing. While the field received the cold shoulder for decades, there were a few scientists and researchers who plodded forward, keeping faith that the idea of artificial neural networks would one day bear fruit. And we are seeing the fruits of deep learning in everyday applications, such as search, chat, email, social media, and online shopping.

The past few weeks have seen major AI announcements by Microsoft, OpenAI, Google, and other organizations. Tech companies are scrambling to solidify their position in the fast-expanding market for large language models (LLM) and generative AI. And as big tech continues to pour more money into the field, competition is gradually becoming polarized between Microsoft and Google. So far, Microsoft has proven to be craftier and more capable in getting LLMs generative machine learning models to work in its products. But the race is not over, and we might yet see Google (or some other company) take the lead.

I've been frequently sounding the alarm on the path that OpenAI has taken since it started its partnership with Microsoft. I've argued that the artificial intelligence lab has gradually swayed from pursuing science to creating profitable products for its main financial backer. OpenAI CEO Sam Altman put some of my doubts to rest this week with a blog post in which he laid out the lab's plan for artificial general intelligence (AGI). Regardless of where you stand on the AGI debate, the post includes some interesting points about how OpenAI plans to tackle the challenges of AI research and product development. And I think this is important because many other research labs will be facing similar challenges in the coming years.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. AI is frequently explained using the categories artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super-intelligence (ASI).[1] Despite this strange conceptual framework providing nothing of real value, it finds its way into many discussions.[2] If unfamiliar with these categories, consider yourself lucky and move on to another, more consequential article. If you are unlucky, I invite you to keep reading.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. The conversation around large language models (LLM) is becoming more polarized with the release of advanced models such as ChatGPT. To clear out the confusion, we need a different framework to think about LLMs, argue researchers at the University of Texas at Austin and Massachusetts Institute of Technology (MIT). In a paper titled "Dissociating language and thought in large language models: a cognitive perspective," the researchers argue that to understand the power and limits of LLMs, we must separate "formal" from "functional" linguistic competence. LLMs have made impressive advances on the former, but still have a lot of work to do on the latter, the researchers say.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. ChatGPT and other large language models (LLM) have proven to be useful for tasks other than generating text. However, in some fields, their performance is confusing. One such area is math, where LLMs can sometimes provide correct solutions to difficult problems while at the same time failing at trivial ones. There is a body of research that explores the capabilities and limits of LLMs in mathematics.

Polanyi's paradox, named in honor of the philosopher and polymath Michael Polanyi, states that "we know more than we can tell."[1] He means that most of our knowledge is tacit and cannot be easily formalized with words.[2] In The Tacit Dimension, Polanyi gives the example of recognizing a face without being able to tell what facial features humans use to make such a distinction. The example describes Gestalt psychology which emerged in the early twentieth century as a theory of perception that rejected the basic principles of elementalist and structuralist psychology as well as functionalist and behavioralist theories of the mind. Gestalt theory emphasizes that conscious humans perceive entire patterns or configurations, not individual components, and cannot always explain what they know. Consider the ancient Chinese game Go, where nobody can define a good move.

The past weeks have seen escalating competition between Microsoft and Google over large language models--or more precisely put, Google trying hard to protect its search business against Microsoft and OpenAI's large language models. The two tech giants are in an intensifying tug of war over how we will access information in the future, matching research with research, product with product, and investment with investment. Since OpenAI released ChatGPT in November, there has been a lot of speculation about the large language model's killer application(s). One of the topics brought up again and again is ChatGPT and other LLMs making Google Search obsolete. I'm still sticking to my previous argument that something like ChatGPT will replace Google Search.

Since OpenAI released ChatGPT, there has been a lot of speculation about what its killer app will be. And perhaps topping the list is online search. According to The New York Times, Google's management has declared a "code red" and is scrambling to protect its online search monopoly against the disruption that ChatGPT will bring. ChatGPT is a wonderful technology, one that has a great chance of redefining the way we create and interact with digital information. It can have many interesting applications, including for online search.

The B2B eCommerce environment has developed significantly over the last decade or so. No matter what sector you focus on, more innovations have emerged that can help to make your business more efficient and effective. However, too many companies still insist on keeping hold of the manual processes, often through budgetary concerns. Yet, this can hold you back from effectively addressing some of the more difficult challenges you face. Adopting automated technology into your practices certainly requires mindful investment.