Mastering one or more programming languages has historically been the gateway to implementing ideas on a computer. Today, that gateway is widening with advances in large language models (LLMs) and artificial intelligence (AI)-powered coding assistants. What matters is no longer just fluency in traditional programming languages but the ability to think computationally by translating problems into forms that can be solved with computing tools. The capabilities enabled by these AI-augmented tools are rapidly leading to the commoditization of computational thinking, such that anyone who can articulate a problem in natural language can potentially harness computing power via AI. This shift is poised to radically influence how we teach computer science and data science in the United States and around the world. Educators and industry leaders are grappling with how to adapt: What should students learn when the hottest new programming language is English? How do we prepare a generation of computational thinkers who need not code every algorithm manually, but must still think critically, design solutions, and verify AI-augmented results? This paper explores these questions, examining the impact of natural language programming on software development, the emerging distinction between programmers and prompt-crafting problem solvers, the reforms needed in computer science and data science curricula, and the importance of maintaining our fundamental computational science principles in an AI-augmented future. Along the way, we compare approaches and share best practices for embracing this new paradigm in computing education.

ChatGPT is all the rage. It is what everyone has been talking about in the last several weeks. In just over a week, it garnered over 1 million users, an incredible achievement for OpenAI, the organization that created it. ChatGPT is an Artificial Intelligence (AI) application that falls under the Generative AI category – GPT stands for Generative Pre-Trained Transformer. Generative AI enable computers to create new content using previously created content, such as text, audio, video, images and code.

Elger: I'm going to talk to you about AI as a service, fast track to AI with serverless. I'm not going to go deep into training models and all of that stuff. Really, the takeaway I hope you get from this talk is that adopting machine learning in your day-to-day work is really not as difficult as you might think. That you maybe come away from this talk able to go and start experimenting at a low cost with AI as a service. Because in a lot of cases, the ability to do machine learning or to run inferences is, these days, just an API call away. At fourTheorem, we do work in the serverless space, obviously, and we work in machine learning. My own particular area of research with regard to machine learning is, how do we apply machine learning to the process of software transformation? Although, as we heard, the monolith is not the enemy, but can that be treated as a big data problem? I'm not going to talk to you about that. Some sausages, some rashers, black pudding, eggs, wash it down with some coffee and some orange juice. I want to go to the shops. I want to buy myself that breakfast. It's going to cost me about £15, £16 to buy these. I'll probably get two breakfasts out of it as well, and I can reuse the coffee. Let's say that I wanted to DIY my own breakfast. I want to build it from scratch. What does that look like? It looks significantly more expensive.

The mass affordability of sequencing enables a paradigm shift from sequencing only those with risk factors (such as someone's family history or medical symptoms) to sequencing proactively to identify risk factors. It will allow every individual to build up genomic data capital, opening the door for new applications and business models across health insurance, care delivery, and everyday life. New approvals & patents - Ava, a Swiss digital healthcare company focused on women's reproductive health, announced that the United States Food and Drug Administration (FDA) clearance for its fertility tracking wearable. BrainQ, an Israeli start-up, announced that the FDA has designated its AI-powered electromagnetic field therapy that aims to enhance recovery and reduce disability after neurological damage caused by stroke as a Breakthrough Device, giving access to the new Medicare Coverage of Innovative Technology (MCIT) pathway. Voluntis (French DTx) announced the issuance of a new patent by the European Patent Office (EPO) for intelligent patient support in drug dosing applied in the field of diabetes for insulin titration support.

Technology advances and marketplace factors are enabling the growing success of conversational AI systems, which can benefit the enterprise across a number of areas. The age of conversational AI is altering the landscape of traditional web and mobile applications. With increased access to cloud computing and sophisticated algorithms, more companies are able to deploy interfaces that combine natural language processing, AI, and machine learning capabilities to better understand and respond to free-form text or voice in an engaging and personalized manner. These systems can transform technology adoption into a conversation rather than an exercise in mastering a user interface. As a result, many enterprises expect conversational AI may first augment, and then displace, a number of traditional web and mobile applications.

It is getting easier and easier to do deep learning (DL). There exist papers, blogs, frameworks, books, courses, newsletters, conferences, and many more resources. If you don't want to implement it yourself, there are machine learning (ML) API services on AWS, GCE, Azure and companies like Clarifai and Bonsai, to name a few. Thinking back to a few years ago, neural nets were sometimes regarded as "just a fad" --I can remember skipping this topic in my grad school ML class because the professor didn't like them and thought they were hyped. So let's talk about some of things that have happened to the field of DL in the last few years that have shifted this view.

Thomas H. Davenport is the President's Distinguished Professor of Information Technology and Management at Babson College and a Fellow at the MIT Initiative on the Digital Economy. We don't typically think of the jobs that we perform as commodities. The Merriam-Webster entry on commodity describes it as "a mass-produced unspecialized product." But most of us view our jobs as specialized or somehow differentiated. We typically believe that we do them differently, and often better, than anyone else with the same job.

This is an exciting time for those of us in computer vision -- we're seeing it merge with AI to enable all kinds of new possibilities. AI needs data with which to learn and process, and as we move closer to more "human"-like AI, it will increasingly need visual data. "This is one of the reasons all the major companies are at war to own the visual data of our activities," said LDV Capital's Evan Nisselson. "To do that, they need to own the camera." Amazon recently added a camera to its Alexa-powered Echo, for example, and Google (Lens) and Facebook recently made new recent augmented reality announcements.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. Economists define commodities as interchangeable goods or homogenous products. Being in the commodity business is all about scale and securing decent returns on increasingly thin margins, which is an unfriendly environment for many organizations.

Google needs to make "Parse for AI" to wedge themselves deeply into apps even when on other's platforms/cloud. I've been interested in this space for a while. A broad prediction I have for the coming years is that, as a developer, you won't need to be proficient in machine learning to take advantage of its power. The technology is becoming increasingly democratized and opening up access to millions of new developers. Eventually, you won't even need to know how to program to perform data analysis with ML.