As we look back, we observe that the last decade has been a great phase for AI and its researchers. This was the time where we saw AI in a more dynamic and evolutionary role, be it in professional or personal lives. However, in a report by BBC, it has been noted that "hype surrounding AI has peaked and troughed over the years as the abilities of the technology get overestimated and then re-evaluated. Where the peaks are known as AI summers, the troughs are termed as AI winter." According to the report, the 10s were arguably the hottest AI summer on record with tech giants repeatedly touting AI's abilities.

Artificial intelligence is having a moment in London. Last week, to coincide with London Tech Week, an annual showcase of the city's digital prowess, London hosted CogX, a 6,000-person-strong event that bills itself as the "Festival of All Things AI," and the AI Summit London, which lays claim to the mantle of "the world's largest AI event for business." The events have non-stop panels, parties, and big-name sponsors like SoftBank, Accenture, IBM and Google. Underpinning much of the buzz over artificial intelligence in London and elsewhere is the implicit premise that AI is the transformative technology of the moment, or maybe of the decade, or even of the century or, well, just about ever. Promises like the AI Summit's claim that the technology goes "beyond the hype" to "deliver real value in business" only drives the corporate feeding frenzy among executives desperate not to be left behind.

Amongst all this hype and bandwagon jumping on Artificial Intelligence (AI), Machine Learning (ML), and Cognitive Technologies is also a sense of unease. How is it that a technology that has roots going back as far as the beginnings of computing is suddenly now the hot "must have" technology that's powering ever-more dramatic amounts of money being pumped into a few skyrocketing startups? The industry has gone through two major waves of AI development and promotion with their own periods of sky-high hype only to sink dramatically back to earth once people realized the limitations of what surely was being hyped as being on the cusp of sentience. And so here we are again, in the "summer" of this wave's AI adoption wondering if this will all last, or if billion-dollar unicorns are being funded in an environment that's sure to pull back the reins of overinflated expectations. As discussed in previous newsletters, podcasts, and research on this subject, an AI Winter is a period of declined interest, funding, research, and support for artificial intelligence and related areas -- in essence, a "chill" on the growth of the industry.

There is a lot of hype today about deep learning, a class of multilayer perceptrons with some 5-20 layers featuring convolutional and polling layers. Many blogs [1,2,3] discuss the structure of these networks, there is plenty code published so I won't get into much detail here. Several tech companies had invested a lot of money into this research and everyone has very high expectations on performance of these models. Indeed they've been winning image classification competitions for several years now and media are reporting superhuman performance on some visual classification tasks once in a while. Now just looking at the numbers from ImageNet competition is not really telling us much on how good these models really are, we can only maybe confirm that they are much better than whatever came before them (for that benchmark at least).

There is a lot of hype today about deep learning, a class of multilayer perceptrons with some 5-20 layers featuring convolutional and polling layers. Many blogs [1,2,3] discuss the structure of these networks, there is plenty code published so I won't get into much detail here. Several tech companies had invested a lot of money into this research and everyone has very high expectations on performance of these models. Indeed they've been winning image classification competitions for several years now and media are reporting superhuman performance on some visual classification tasks once in a while. Now just looking at the numbers from ImageNet competition is not really telling us much on how good these models really are, we can only maybe confirm that they are much better than whatever came before them (for that benchmark at least).