Microsoft UK has reported an "encouraging increase" in the use of artificial intelligence (AI) technologies in healthcare. In a survey of the use of AI in UK industry, 46% of healthcare leaders reported their organisation used the technology in some capacity, reflecting an 8% increase compared to 2018. The biggest growth areas reported were research-level AI, which grew 13% in the past 12 months. Robot process automation (RPA) and general automation both increased by 10%, while the use of voice recognition technology increased by 9%. The study, conducted by YouGov, included the input of some 1,000 business leaders and 4,000 employees.

Five Dutch companies Ahold Delhaize, ING, KLM, NS and Philips aim to further boost the AI ecosystem in the Netherlands by accelerating and promoting the development of AI technology and nurturing AI talent in the country. This effort will add educational capacity, foster the development of the AI community in the Netherlands and reiterate the position of the Netherlands as a competitive and relevant global AI hub. The goal of Kickstart AI, is to bridge the AI gap between the Netherlands and other countries, like the UK, the US and China, that have made notable progress in this area. In order to keep the country's position as a pioneer and inventor of technologies, the Dutch government, companies, organizations and universities have ground to cover in terms of structural investments and availability of global AI talent. The five companies "kickstarting" AI are, for the first time, uniting forces in this kind of joint initiative and taking highly needed decisive action.

Five Dutch companies Ahold Delhaize, ING, KLM, NS and Philips aim to further boost the AI ecosystem in the Netherlands by accelerating and promoting the development of AI technology and nurturing AI talent in the country. This effort will add educational capacity, foster the development of the AI community in the Netherlands and reiterate the position of the Netherlands as a competitive and relevant global AI hub. The goal of Kickstart AI, is to bridge the AI gap between the Netherlands and other countries, like the UK, the US and China, that have made notable progress in this area. In order to keep the country's position as a pioneer and inventor of technologies, the Dutch government, companies, organizations and universities have ground to cover in terms of structural investments and availability of global AI talent. The five companies "kickstarting" AI are, for the first time, uniting forces in this kind of joint initiative and taking highly needed decisive action.

Because of this healthcare worker shortage, some US hospitals with patient populations dominated by the older, the sicker, and the inactive are even closing their doors. According to analyses conducted by Mercer and the Association of American Medical Colleges, by 2025 – the year the World Economic Forum predicts machines will do half of human workers' tasks, no less – healthcare will face a shortfall of more than 731,000 total jobs – including roles like doctors, surgeons, nursing assistants, nurse practitioners, lab technicians, and home health aides. Talk of robots replacing human doctors misses the point. When it comes down to it, even the most advanced AI tool is extremely limited, knowing a great deal about a very narrow area of knowledge – kind of like if someone could recite every work of Shakespeare from memory but didn't know Shakespeare's first name. Humans, in contrast, know a little bit about many different things – like if a doctor knew how to chop garlic, how to perform heart surgery, and how to write in iambic pentameter.

Among the 16 winners is the project "REIF: Resource-efficient, Economic and Intelligent Foodchain", which aims to revolutionise the food industry in Germany to guarantee a supply that is as waste-free as possible. The CSCP is a partner of this research project which investigates the potential of AI to optimise the planning and control processes in the food industry. The CSCP will support the effective and efficient integration of all relevant stakeholders into the REIF ecosystem, both during and after the project. Also, it will contribute to enabling the participating companies to adapt operational processes and organisational learning through training and further education. By analysing the needs of manufacturers, retailers and end consumers, the requirements for AI-based services will be sharpened and the range of solutions and concepts improved to ensure connectivity and further use of the project results.

Patents are filed in China for international protection of inventions under PCT route after domestic patent filing. The first patent application is filed in home country to secure a patent priority date. Thereafter, within 12 months of the priority date, a PCT international phase application is filed with the WIPO. The PCT international patent application provides a time period of almost 30 to 31 months from the priority date to file the same patent application in multiple countries, including, India, China, Singapore, and the like. China and India amended their patent laws to fulfill the obligation of TRIPS agreement.

Not a week passes without an ethical misstep by Big Tech. From Facebook's personal data overreaches to thousands of e-commerce sites that trick people into superfluous purchases to cities implementing facial-recognition systems without consent, the tech industry continues to stress-test trust. In response, ethical guidelines have flourished. Whether a short checklist, visual principles, or lengthy treatise, most agree on core principles of privacy, safety and security, transparency, fairness, and autonomy. But despite the efforts of think tanks, tech companies, and government agencies, the principles haven't been so easy to put into practice.

Cambridge, UK-based biotech startup Mogrify, which is working on systematizing the development of novel cell therapies in areas such as regenerative medicine, has closed an initial $16 million Series A. The raise follows a $4M seed in February -- taking its total raised to date to $20M. Put simply, Mogrify's approach entails analysis of vast amounts of genomic data in order to identify the specific energetic changes needed to flip an adult cell from one type to another without having to reset it to a stem cell state -- with huge potential utility for a wide variety of therapeutic use-cases. "What we're trying to do with Mogrify is systematize that process where you can say here's my source cell, here's my target cell, here are the differences between the networks… and here are the most likely points of intervention that we're going to have to make to drive the fate of an adult cell to another adult cell without going through a stem cell stage," says CEO and investor Dr Darrin Disley. So far he says it's successfully converted 15 cells out of 15 tries. "We're now rapidly moving those on through our own programs and partnership programs," he adds.

I bought the first version of this book, and now also the second. The new version is very comprehensive. If you are using Python - it's almost a reference. I also like the emphasis on neural networks (and TensorFlow) - which (in my view) is where the Python community is heading. I am also planning to use this book in my teaching at Oxford University. The data pre-processing sections are also good. I found the sequence flow slightly unusual - but for an expert level audience, it's not a major issue.

What a time this is to be working in the machine learning field! The last few years have been a dream run for anyone associated with machine learning as there have been a slew of developments and breakthroughs at an unprecedented pace. There's just one thing to keep in mind here – these breakthroughs did not happen overnight. It took years and in some cases, decades, of hard work and persistence. We are used to working with established machine learning algorithms like neural networks and random forest (and so on). We tend to lose sight of the effort it took to make these algorithms mainstream. To actually create them from scratch. The people who lay the groundwork for us – those are the true heroes of machine learning.