For some, AI can help remove the drudgery from daily work. For many others, though, AI is not an assistant. For some, AI can help remove the drudgery from daily work. For many others, though, AI is not an assistant. Forget the AI job apocalypse.

Github Co-Pilot is a cutting-edge AI-poGithubred tool developed by GitHub and OpenAI that offers an innovative way for developers to write code. Using advanced machine learning algorithms, Co-Pilot provides intelligent suggestions and auto-completion for code snippets, making it easier and faster for programmers to write complex programs. One of the critical advantages of Co-Pilot is its ability to learn and adapt to the coding patterns and styles of individual developers. As developers work with Co-Pilot, it becomes more familiar with their coding habits and can offer more personalized suggestions and assistance. Moreover, Co-Pilot is built on OpenAI's state-of-the-art GPT language model, which is trained on a massive corpus of data and can understand natural language queries and generate human-like responses.

Seattle-based Zeitworks, which is launching its private beta today after raising a $4.5 million seed round in 2020, wants to give enterprises data-driven tools for improving the productivity of their teams and streamline their business operations. That's a market that's seeing quite a bit of growth right now, especially given how the pandemic has made remote work a standard business practice and how the overall talent crunch is forcing many businesses to do more with fewer employees. The overall idea here is to give businesses better insights into how teams work and where there are opportunities for improving business processes beyond simply using automation. "The problem that we're really addressing is that there's teams and companies in just about every industry who execute all kinds of repetitive business processes every day– and to be clear, it's business processes executed by humans," Zeitworks CEO and co-founder Jay Bartot told me. "Think about processing bank loans or insurance claims or HR onboarding of new employees, moving information from system to system. Oftentimes, those systems aren't interconnected or don't have APIs. The problem that we're solving is that the majority of these processes can't be optimized because they're undocumented and unmeasured. Unsurprisingly, understanding these processes is at the core of Zeitworks' product. But since these processes aren't documented, you can't exactly build a rule-based engine around discovering what people are doing. Instead, the company uses an AI-driven task mining system that uses signals from a wide variety of sources, mostly with a focus on the desktop applications these users interact with during their daily work. Bartot actually noted that he prefers the term'process intelligence' over'task mining,' given that task mining tends to be associated with creating RPA bots more than empowering teams and helping them work better. Now, in order to do all of this, Zeitgeist has to run its agent on an employee's desktop and those users' daily work is then tracked with quite a bit of granularity. Microsoft, with its Productivity Score, does something similar, but the company also faced quite a bit of backlash over it, given that managers could drill down to the individual employee and see how many emails they sent, chats they participated in, etc. The company later made some changes that put the focus more on the organizational level and away from individual users. "In our world, the kinds of productivity scores that we are recording are around this repetitive work -- the fact that people are processing bank loans or you know insurance claims repeatedly is a fundamental part of what we're measuring and what we're doing with pattern recognition," Bartot explained when I asked him about the potential for backlash. "So the productivity scores are really geared towards that specific kind of repetitive work.

How much do managers know about how their teams work? We recently ran a research study involving 14 teams comprising 283 employees in four Fortune 500 companies. When managers were asked about their teams' work, on average they either did not know or could not remember 60% of the work their teams do. In one extreme instance, a manager in our study could describe only 4% of their team's work. The cost of managers not knowing this gap exists can be high, even in teams as small as five members, and is therefore applicable to any company, big or small.

But the impact on jobs has not yet arrived in most organizations. As recently as 2017, headlines such as "Bosses Believe Your Work Skills Will Soon Be Useless" (from the The Washington Post) were common. Oxford University researchers argued in 2013 that 47% of U.S. jobs were at risk of loss to automation. MIT launched its institute-wide task force on the future of work in 2018. Leaders around the world began to consider how their organizations would be different when thousands of their employees' jobs are automated away.

In response to the COVID-19 outbreak, AI company BioMind developed a solution (BioMind AI-COVID) with the aim of diagnosing COVID-19 Pneumonia in a fast, accurate and cost-effective manner. It has since been deployed in hospitals across China, and is intent on expanding its footprint globally. Ian Bolland found out more. Give us some background about BioMind – who developed it? BioMind is an award-winning Artificial Intelligence company that specialises in creating predictive applications to assist doctors in their daily work.

As we enter a new decade, we take with us the growing challenges we face in many fields, including artificial intelligence and conducting business while ensuring human rights. These hot topics are not going away any time soon. With the speed of innovation and technology, the responsibility of keeping up with development and regulating practices is all the more crucial to ensure a just world. Our upcoming winter academies on AI and international law, and due diligence as a key to responsible conduct, will empower you with the skills and knowledge you need to tackle those issues in your daily work. Winter academy on Artificial Intelligence and International law (20 – 24 January) 2020 will be a critical year to set the tone for the next decade of innovations in Artificial Intelligence (AI), one of the most complex technologies to monitor or regulate.

In a previous post, I expressed my happiness that I got to present at ML in PL in Warsaw. I had the opportunity to take a step back and reflect a bit on the ethics of what we do as practitioners of data science and builders of machine learning models. It's an important topic and doesn't receive the attention that it should. The algorithms we build affect lives. I have researched this topic quite a lot, and during that time I have found a number of stories that made a huge impression on me.

Discussions seem to be popping up everywhere from industry events to articles in mainstream business magazines about the future of medicine and whether artificial intelligence (AI) and machine learning will displace the work being done by researchers and doctors. A recent interview in The New Yorker even suggested that radiologist training should be halted, since deep learning will be doing a better job than professionals within the next five years. While it's true that artificial intelligence and computer-based algorithms are making their way into both the lab and clinical practice, the adoption of these new technologies will not replace the work of the researchers themselves. On the contrary: it'll enable them to become more effective than ever before. Big data is getting bigger by the minute.

Artificial intelligence (AI) had a coming out party of sorts in 2016. Even though it has been in development for decades, this year, with the perfect combination of cheap computing power and access to increasing amounts of data, it seems AI's time has come. Its first foray in business has been directed at making salespeople more efficient at every level of the sales workflow. If you think about it, it makes sense to start with the part of the company that drives revenue. Certainly the vendors recognize that, says Alan Lepofsky, an analyst at Constellation Research, who is working on the impact of AI on work.