Mark Zuckerberg and his wife, Priscilla Chan, have laid out plans to invest $3bn over the next 10 years with the not insignificant goal of tackling all diseases. "Can we cure, prevent or manage all disease by the end of this century?" The Chan Zuckerberg Initiative will focus on some of the world's biggest killers, including heart disease, cancer, infectious diseases and neurological diseases. One of its biggest investments is to be a $600m "Biohub" at UCSF, which will bring together scientists and engineers from Stanford, Berkeley and UCSF – who haven't collaborated in this way before – to develop tools to treat diseases. The second focus will be transformative technology, all of which will be made available to all scientists everywhere. "Throughout the history of science, most of the major scientific breakthroughs have been preceded by some new tool and technology that allows you to see in new ways," explained Zuckerberg.

It has been said that to give a man a fish is to feed him for a day, whereas to teach a man to fish is to feed him for life. Forward-looking financial service companies are similarly finding that giving computers instructions is not nearly as fruitful as teaching them to write their own. From assessing credit risks to beefing-up the security of their own networks, fintech startups, in particular, are turning to machine learning finance-based solutions in order to work smarter rather than harder. Considering that over 200 leading financial institutions will attend the upcoming October 2016 Machine Learning Fintech Conference, investment in this subset of artificial intelligence (AI) seems to be a wise move, indeed, for companies that don't want to be left behind. With leading banks starting to invest in AI, and machine learning in particular, fintech companies will be significantly disadvantaged if they fail to do likewise.

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Tech giant Google is teaming up with gaming publisher Blizzard to improve artificial intelligence (AI) through one of its games: StarCraft. However, it seems other games may fit the "teaching" bill as well. According to Google, Blizzard announced in the annual BlizzCon that it will release tools that will allow third parties to teach AI how to play their hit game StarCraft 2. This is in collaboration with Google's DeepMind project, and the tools will be using the DeepMind platform. The DeepMind team said StarCraft is an "interesting platform" to develop current research on AI. The gameplay itself may be a useful environment to simulate the "messiness" of the real world.

IBM's Watson system will help doctors diagnose rare illness faster by scanning through huge medical records to pull out relevant information. "We don't need more physicians, we need more IT power." It's a controversial statement for anyone with an interest in healthcare to make, let alone for a senior doctor heading up a centre for rare and undiagnosed diseases. But Dr Jürgen Schäfer, in charge of tackling the most mysterious conditions that arrive at the centre in the University Hospital in the German town of Marburg, is used to solving seemingly intractable problems. But now, rather than identifying the illnesses that have baffled numerous doctors before him, Schäfer - sometimes called the German'Dr House' -- has worked out how to crack another tricky medical problem: how to treat a spiralling number of patients, each with a lengthy and complex health history, without employing a whole new team of doctors.

Machine learning is concerned with one aspect of this: given some AI problem that can be described in discrete terms (e.g. Typically some outside process is needed to judge whether the action was correct or not. In mathematical terms, it's a function: you feed in some input, and you want it to to produce the right output, so the whole problem is simply to build a model of this mathematical function in some automatic way. To draw a distinction with AI, if I can write a very clever program that has human-like behavior, it can be AI, but unless its parameters are automatically learned from data, it's not machine learning.

Much as Linux, Windows, and iOS coordinate the execution of computing applications, laws coordinate the execution of human society. When new kinds of interactions emerge – sharing our airspace with private drones, for example, or algorithmic trading on financial markets – new laws are encoded to regulate those activities. Laws respond to conflicts of interest, keep criminals and cheats in check, and temper the abuse of power. "Space law, tax law, online law, regulations for autonomous vehicles and artificial intelligence... if you think about laws and how they evolve to match the complexity of the functions they coordinate, laws become an interesting problem for complex systems science," says SFI President David Krakauer. During SFI's 2016 Applied Complexity Network (ACtioN) and Board of Trustees Symposium April 3-5, themed "Law OS," Krakauer announced the beginning of a new research program at SFI on "Complexity and the Law."

Recently, I needed to book a lunch meeting. To help coordinate, I asked Amy to assist and cc'd her on the email. "Amy," I wrote, "please help us find a time to meet. Let's plan for sushi at Tokyo Express on Spear Street." Amy looked at my calendar, found an open time suitable for everyone invited, and booked the meeting.

Global awareness of data privacy and its implications is improving, not least due to a spate of high profile image hacks. But this has been slow to translate directly into changed behaviour, as in many instances there has been no alternative to storing and archiving images other than on global online services, where data can be mined, hacked and shared. Pimloc, which intends to provide that alternative, was founded in March this year by serial entrepreneur Simon Randall. The founding team includes some of the world's foremost thinkers in deep learning visual technology and computer vision application development across a range of fields. The firm has already completed an oversubscribed seed funding round lead by staff and UK business angels.

SAP SE (NYSE: SAP) today introduced three initiatives to make its business applications more intelligent and empower its ecosystem to build machine learning (ML) applications for customers. Spanning its own solutions, partner programs and educational offerings, these programs will help accelerate ML adoption across SAP's global customer base. This announcement was made at the SAP TechEd conference, being held November 8-10, 2016, in Barcelona. First, SAP has unveiled new intelligent business applications. A new solution, "brand intelligence," is supposed to analyze brand exposure in video and images by leveraging deep learning.