Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. Why we should be intentional about data scaling. Large language models (LLMs) have revolutionized the AI landscape, demonstrating remarkable capabilities across a wide range of tasks. Each new model seemingly reinforces the notion that modern transformer-based AI can conquer any challenge if armed with sufficient compute and data. However, while scaling has accelerated certain applications, such as robotics, it has yet to show significant impact in others, such as identifying misinformation.

The school year starts soon for many students, and ChatGPT has announced a new "study mode" that aims to prevent--or at least, encourage against--students taking homework shortcuts. The mode is designed around the Socratic method, so when activated, OpenAI's generative AI chatbot rejects direct requests for answers, instead guiding the user with open-ended questions. The new study mode is available to most logged-in users of ChatGPT, including those on the free version. OpenAI has significantly disrupted the education system over the past few years, with students becoming some of the earliest adopters of ChatGPT. Even so, OpenAI claims the bot is currently an overall boon to learners--if asked to roleplay as a synthetic tutor.

Emanuel Younanzadeh is VP Marketing at The Modern Data Company. Artificial intelligence (AI) continues its rise to prominence within the business world. The number of companies using AI today and the range of problems AI is being applied to are both increasing steadily. However, there is one issue that is plaguing AI just as much as it has plagued analytics of all kinds over the years--data quality. Organizations put tremendous resources behind ensuring the quality of their data.

Every artificial intelligence(AI) problem is a new universe of complexities and unique challenges. Very often, the most challenging aspects of solving an AI problem is not about finding a solution but understanding the problem itself. As paradoxically as that sounds, even the most experienced AI experts have been guilty of rushing into proposing deep learning algorithms and exoteric optimization techniques without fully understanding the problem at hand. When we think about an AI problem, we tend to link our reasoning to two main aspects: datasets and models. However, that reasoning is ignoring what can be considered the most challenging aspect of an AI problem: the environment.

Elon Musk's problems are bigger and more important than yours. While most of us are consumed with our day-to-day activities, Musk has been anointed by a higher power to save us all from ourselves. He's here to ensure we eliminate car accidents, make traffic a thing of the past, solve autism (his words, not mine), connect human brains to machines, fill the night sky with satellites so everyone can have internet access, and colonize Mars. He doesn't exactly know how we're going to accomplish all those things, but he has more than enough money to turn any and every single good idea he's ever had into a functioning industry. Who cares if Tesla's 10, 20, or 100 years away from actually solving the driverless car problem?

I have been asked many times during the past month whether the heightened pressure that enterprises are now facing as a result of the Covid-19 pandemic will cause them to short-cut aspects such as responsible machine learning in order to get pilots into production more quickly. This is certainly a possibility, but in my opinion, people's memories of the actions that enterprises are taking now will run much deeper than many of the better-planned projects that came before the pandemic or have yet to start. More organisations will therefore aim to get artificial intelligence (AI) right during the crisis as well. As practitioners get going in this area, here are a few things to consider. One global bank I spoke to recently has just put in place a policy that no AI model can move into production without some interpretability and bias controls built into the lifecycle of the application.

Tackling a machine learning problem might feel overwhelming at first. What model to choose?, which architecture might work best? In a process that is mostly driven by trial and error experimentation, those decisions result incredibly important. One aspect that really helps to navigate that universe of decisions is to clearly understand the nature of the problem. In machine learning scenarios, an important part of understanding the problem is based on understanding its environment.

As the Defense Advanced Research Projects Agency (DARPA) continues exploring emerging technologies for the Department of Defense, it's considering the implementation of artificial intelligence (AI) to tackle electric grid cybersecurity and get ahead of 5G deployment. "In the area of cyber operations, we have a program RADICS [Rapid Attack Detection, Isolation and Characterization Systems], which is designed to help recover critical portions of the power grid in the event of a full blackout caused by malware," Scherlis explained. The program partners with power companies, the National Guard, the Department of Homeland Security, and the Department of Energy to enable a "black start" recovery, a restart to the electric grid without external power, in the event of a cyberattack. Scherlis mentioned the program in context of AI, but the program is generally building new tech to accelerate recovery through improved situational awareness, network isolation, and the ability to adapt to changing cyber situations. "The idea of this program is how can we understand what is the state of affairs in the grid and incrementally restore service, purge the malware, and bring service back to the most critical assets and then stage that out beyond. They've done a number of field trials," Scherlis added.

Predictive maintenance is one of the most exciting applications of digital technology in manufacturing. Simply put, predictive maintenance is the use of new and historical machine data to understand and, ideally, anticipate performance problems before they happen. Using sophisticated machine learning and AI techniques to analyze the data generated in the modern factory, predictive analytics can decrease downtime, optimize asset performance, and increase the lifespan of machines. The promises made on behalf of predictive maintenance (PdM) are big. Smart machines that flag performance issues before they happen.

In 2012 or so, if you'd asked most people in tech about'neural networks', if they had any answer at all they might well have said that it was an obscure idea from the 1980s that had never really worked - rather like VR. Then, in 2013, Imagenet gave us an explosive realisation that this could work now - again, rather like VR in 2013. Since then, the tech industry has been remaking itself around machine learning. There's a naive view that'Google will have all the data' or China will have all the AI' or'Data is the new oil', but it's more interesting to look at how many different kinds of deployment are now happening. The first phase was the creation of companies building platforms (or'primitives' or'substrates') for specific low-level ML applications - image recognition, voice recognition, sentiment analysis etc.