The main challenge that organisations face in implementing machine learning is the complex infrastructure or workload needs. A whopping 90% of CXOs feel the same way. Into the details of this – 88% struggle with integration and compatibility of AI/ML technologies, while 86% struggle with the frequent updates that are required for data science tooling. Such stats by the DataRobot 5 Latest Trends in Enterprise Machine Learning 2021 report state that many organisations do have a difficult time keeping up with ML. This implores the question – is ML really overhyped?

GPT-3 made headlines in the machine learning community recently over viral videos showing human language-to-code translation. The model could be used to automate many redundant and repeated coding, for example in HTML/CSS or in the construction of a simple neural network. As many others have pointed out, GPT-3 is only a tool, and very limited by its training data. Much of the more sophisticated things developers want to do -- for instance, add some specific momentum-based animation to a site -- cannot be done by GPT-3 because it a) isn't advanced enough, b) doesn't have enough training data, and c) arguably isn't "creative". GPT-3 may become a helpful tool to help developers spend less time on retyping old commands and more time on brainstorming creative infrastructure designs and debugging complex, cross-system bugs, but it's in no way a threat to the livelihoods of programmers.

Banking communications and press releases show conversational interfaces accounting for 38.87% of the AI use-cases at banks. In truth, most chatbots are pilot projects with little to no evidence of ROI; they're touted in the press to make banks appear more modern and convenient to customers. Emerj's AI in Banking Vendor Scorecard and Capability Map found conversational interface vendors score lowest in terms of funding and the AI talent they employ (2.4 out of 4.0). The average customer service vendor in banking raises $16 million, far less than the average vendor in financing and loans ($49 million), fraud and cybersecurity ($48 million), and compliance ($44 million). Companies raise more money when their products have traction, and conversational interface vendors make up only 5.5% of the total funding for AI vendors.

Artificial intelligence (AI) is in vogue. It's almost impossible to read an article in any media outlet that doesn't mention AI and the possibility it will reshape the world in which we live. In fact, according to research conducted by AT&T, AI has the potential to double GDP growth across geographies by 2035. Consumers are already interacting with a variety of low-level AI assistants, such as Siri, Cortana, and Alexa. With respect to the telecom sector, AI – supported by machine learning (ML) – is fundamental to controlling and operating communications networks of the future.

Artificial intelligence (AI) is in vogue. It's almost impossible to read an article in any media outlet that doesn't mention AI and the possibility it will reshape the world in which we live. In fact, according to research conducted by AT&T, AI has the potential to double GDP growth across geographies by 2035. Consumers are already interacting with a variety of low-level AI assistants, such as Siri, Cortana, and Alexa. With respect to the telecom sector, AI – supported by machine learning (ML) – is fundamental to controlling and operating communications networks of the future.

You might think you can just apply some machine learning algorithm you've heard about to your problem, but chances are it won't work nearly as well as the blog post or paper you got it from. A lot of details never make it to papers; they exist solely as institutional knowledge among professionals in the field. You'll need to spend a lot of time configuring the algorithm for your problem, even if your problem is almost identical to the original you're working from. You'll need to tune hyperparameters, find the right architecture, pre-process your data in weird ways, maybe even restate parts of your problem… You can't just throw your problem at an existing algorithm; you'll either need extensive experience or a lot of trial and error.

Platformers and action-adventure games have high-dimensional state spaces with difficult, non-linear constraints on character movement; even worse, game environments often respond to the player in complex ways that can cause exponential expansion of the planning search space. Planning problems in these high-dimensional spaces generally require domain-specific knowledge and manually abstracted models of game rules to replicate the intuition of human designers or playtesters. In this work, we outline a system for modeling these complex games at a precise and low level in terms of hybrid automata. With this representation, standard incremental search algorithms can be used to answer reachable-region queries, taking advantage of the domain information embedded in the system.

In the end, I suppose it's less interesting to me to look at the sheer amount of machine learning hype than at its content. Like, almost everyone in the 1950s knew that computers were going to be important, and of course they were right, but they were often wildly wrong about the reasons (e.g., dramatically underestimating the difficulty of humanoid robots, while failing to foresee PCs or the Internet). There's no doubt in my mind that people 30 years from now will agree with us about the central importance of ML, but which aspects of ML will they rage at us for ignoring, or laugh at us for obsessing about when we shouldn't have? I don't know the answers to those questions, but I know that those are the things I'd like to know.