Ever since ChatGPT captured our imaginations, people have been contemplating its pending impact on the business world. This week these thoughts became a reality, with Google and Microsoft embedding artificial reality (AI) features into their business productivity suites. Microsoft took another major step by releasing AI Copilot for Power Apps, Microsoft's low-code platform. Power Apps can connect far and beyond the Microsoft ecosystem, with almost 1,000 built-in connectors to everything from Salesforce to on-prem and Amazon Web Services. With one swift move, AI has been integrated into the day-to-day workflows of the world's largest organizations. This is an amazing achievement, and other low-code/no-code platforms will surely try to catch up quickly.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! ManageEngine commissioned Vanson Bourne to conduct a global study to examine the role of IT and how it will continue to evolve in the future of work. The report discovered that everyone across the enterprise, not just IT, has a stake in how technology is chosen, deployed, configured and used. Most departments outside IT -- particularly quality control (24%) and finance (21%) -- are using artificial intelligence/machine learning (AI/ML).

Early last year, a Microsoft research project dubbed DeepCoder announced that it had made progress creating AI that could write its own programs. Such a feat has long captured the imagination of technology optimists and pessimists alike, who might consider software that creates its own software as the next paradigm in technology -- or perhaps the direct route to building the evil Skynet. As with most machine learning or deep learning approaches that make up the bulk of today's AI, DeepCoder was creating code that it based on large numbers of examples of existing code that researchers used to train the system. The result: software that ended up assembling bits of human-created programs, a feat Wired Magazine referred to as "looting other software." And yet, in spite of DeepCoder's PR faux pas, the idea of software smart enough to create its own applications remains an area of active research, as well as an exciting prospect for the digital world at large.

Early last year, a Microsoft research project dubbed DeepCoder announced that it had made progress creating AI that could write its own programs. Such a feat has long captured the imagination of technology optimists and pessimists alike, who might consider software that creates its own software as the next paradigm in technology -- or perhaps the direct route to building the evil Skynet. As with most machine learning or deep learning approaches that make up the bulk of today's AI, DeepCoder was creating code that it based on large numbers of examples of existing code that researchers used to train the system. The result: software that ended up assembling bits of human-created programs, a feat Wired Magazine referred to as "looting other software." And yet, in spite of DeepCoder's PR faux pas, the idea of software smart enough to create its own applications remains an area of active research, as well as an exciting prospect for the digital world at large.

Early last year, a Microsoft research project dubbed DeepCoder announced that it had made progress creating AI that could write its own programs. Such a feat has long captured the imagination of technology optimists and pessimists alike, who might consider software that creates its own software as the next paradigm in technology – or perhaps the direct route to building the evil Skynet. As with most machine learning or deep learning approaches that make up the bulk of today's AI, DeepCoder was creating code that it based on large numbers of examples of existing code that researchers used to train the system. The result: software that ended up assembling bits of human-created programs, a feat Wired Magazine referred to as'looting other software.' And yet, in spite of DeepCoder's PR faux pas, the idea of software smart enough to create its own applications remains an area of active research, as well as an exciting prospect for the digital world at large.

Early last year, a Microsoft research project dubbed DeepCoder announced that it had made progress creating AI that could write its own programs. Such a feat has long captured the imagination of technology optimists and pessimists alike, who might consider software that creates its own software as the next paradigm in technology – or perhaps the direct route to building the evil Skynet. As with most machine learning or deep learning approaches that make up the bulk of today's AI, DeepCoder was creating code that it based on large numbers of examples of existing code that researchers used to train the system. The result: software that ended up assembling bits of human-created programs, a feat Wired Magazine referred to as'looting other software.' And yet, in spite of DeepCoder's PR faux pas, the idea of software smart enough to create its own applications remains an area of active research, as well as an exciting prospect for the digital world at large.