While it might sound like something out of an episode of Black Mirror, scientists have warned that AI might be coming to take your job. Microsoft researchers have revealed the 40 jobs most likely to be pushed out by artificial intelligence - and the 40 most likely to remain human. And it's bad news for anyone who has been brushing up on their language skills, since interpreters and translators are right at the top of the list. Historians, writers and authors, political scientists, and journalists are also likely to face increasing automation in the coming years. However, it isn't just jobs involving reading and writing that could be on the chopping block.

OpenAI's ChatGPT isn't just good at generating coherent text responses to natural language prompts -- it can also play a role in human-to-robot interactions and use sensor feedback to write code for robot actions. Microsoft recently conducted research to "see if ChatGPT can think beyond text, and reason about the physical world to help with robotics tasks." The aim was to see if people can use ChatGPT to instruct robots without learning programming languages or understanding robotic systems. In Depth: These experts are racing to protect AI from hackers. "The key challenge here is teaching ChatGPT how to solve problems considering the laws of physics, the context of the operating environment, and how the robot's physical actions can change the state of the world," a team from Microsoft Autonomous Systems and Robotics Research note in a blogpost.

ChatGPT is best known as an AI program capable of writing essays and answering questions, but now Microsoft is using the chatbot to control robots. On Monday, the company's researchers published(Opens in a new window) a paper on how ChatGPT can streamline the process of programming software commands to control various robots, such as mechanical arms and drones. "We still rely heavily on hand-written code to control robots," the researchers wrote. Microsoft's approach, on the other hand, taps ChatGPT to write some of the computer code. ChatGPT can do this because the AI model was trained on huge libraries of human text--including the code for software programs.

Significant advances in AI have also enabled Microsoft to bring new capabilities to customers through our products and services, including GitHub Copilot, an AI pair programmer capable of turning natural language prompts into code, and a preview of Microsoft Designer, a graphic design app that supports the creation of social media posts, invitations, posters, and one-of-a-kind images. These offerings provide an early glimpse of how new AI capabilities, such as large language models, can enable people to interact with machines in increasingly powerful ways. They build on a significant, long-term commitment to fundamental research in computing and across the sciences, and the research community at Microsoft plays an integral role in advancing the state of the art in AI, while working closely with engineering teams and other partners to transform that progress into tangible benefits. In 2022, Microsoft Research established AI4Science, a global organization applying the latest advances in AI and machine learning toward fundamentally transforming science; added to and expanded the capabilities of the company's family of foundation models; worked to make these models and technologies more adaptable, collaborative, and efficient; further developed approaches to ensure that AI is used responsibly and in alignment with human needs; and pursued different approaches to AI, such as causal machine learning and reinforcement learning. We shared our advances across AI and many other disciplines during our second annual Microsoft Research Summit, where members of our research community gathered virtually with their counterparts across industry and academia to discuss how emerging technologies are being explored and deployed to bring the greatest possible benefits to humanity.

Like many students around the world, Eithne, 14, in Chorley, United Kingdom, was struggling to keep up in math at school after more than a year of COVID-19 related disruptions. In June 2021, her parents signed her up for a summer program offered by Eedi, an online math tutoring service. "Just dealing with lockdown, she hadn't had enough of a really good background," said her mother, Arianna. "She missed most of the Year 7 Maths, then Year 8. So, we thought, 'Let's give it a go, let's see where she needs a bit of help.'" Newly enrolled students on Eedi are asked to take a dynamic quiz of 10 multiple choice diagnostic questions that the service uses to learn where students struggle most in math.

Like many students around the world, Eithne, 14, in Chorley, United Kingdom, was struggling to keep up in math at school after more than a year of COVID-19 related disruptions. In June 2021, her parents signed her up for a summer program offered by Eedi, an online math tutoring service. "Just dealing with lockdown, she hadn't had enough of a really good background," said her mother, Arianna. "She missed most of the Year 7 Maths, then Year 8. So, we thought, 'Let's give it a go, let's see where she needs a bit of help.'" Newly enrolled students on Eedi are asked to take a dynamic quiz of 10 multiple choice diagnostic questions that the service uses to learn where students struggle most in math. This information allows the service to place students on a learning pathway to overcome those specific obstacles, or misconceptions.

The researchers pored through nearly 50,000 medical records and found that the software had recommended Black patients for additional care about half the time they should have, while white patients were recommended for additional care at a far higher rate. The reason, Gray explained, was that the algorithms factored in medical histories in predicting how much each patient was likely to cost the health care system if left untreated. This meant that white patients, who typically have better access to health care due to a variety of factors rooted in systemic racism, were given priority for certain treatments.

Microsoft researchers have been working on deep learning model that was trained to find software bugs without any real-world bugs to learn from. While there are dozens of tools available for static analysis of code in various languages to find security flaws, researchers have been exploring techniques that use machine learning to improve the ability to both detect flaws and fix them. That's because finding and fixing bugs in code can be hard and costly, even when using AI to find them. Every remote worker should consider a virtual private network to stay safe online. Researchers Microsoft Research Cambridge, UK have detailed their work on BugLab, a Python implementation of "an approach for self-supervised learning of bug detection and repair".

Editor's Note: In the diverse and multifaceted world of research, individual contributions can add up to significant results over time. In this new series of posts, we're connecting the dots to provide an overview of how researchers at Microsoft and their collaborators are working towards significant customer and societal outcomes that are broader than any single discipline. Here, we've curated a selection of the work Microsoft researchers are doing to advance responsible AI. Responsible AI is really all about the how: how do we design, develop and deploy these systems that are fair, reliable, safe and trustworthy. And to do this, we need to think of Responsible AI as a set of socio-technical problems.

As the recently released GPT-3 and several recent studies demonstrate, racial bias, as well as bias based on gender, occupation, and religion, can be found in popular NLP language models. But a team of AI researchers wants the NLP bias research community to more closely examine and explore relationships between language, power, and social hierarchies like racism in their work. Published last week, the work, which includes analysis of 146 NLP bias research papers, also concludes that the research field generally lacks clear descriptions of bias and fails to explain how, why, and to whom that bias is harmful. "Although these papers have laid vital groundwork by illustrating some of the ways that NLP systems can be harmful, the majority of them fail to engage critically with what constitutes'bias' in the first place," the paper reads. "We argue that such work should examine the relationships between language and social hierarchies; we call on researchers and practitioners conducting such work to articulate their conceptualizations of'bias' in order to enable conversations about what kinds of system behaviors are harmful, in what ways, to whom, and why; and we recommend deeper engagements between technologists and communities affected by NLP systems."