We're all familiar with the story – sophisticated cyber-attacks are increasing in number and complexity, adding to the workload of already beleaguered security professionals, and helping fuel a surge in cybersecurity job postings. But these positions are incredibly tough to fill because of the insufficient supply of experienced professionals and lack of new talent. In fact, a Stanford University study shows the cybersecurity skilled personnel gap stands at 200,000 unfilled jobs. Between the challenge of choosing the right tool amongst a myriad and the challenge of finding skilled security personnel, you have to wonder: how can companies defend themselves in this increasingly complex threat landscape? Machine learning is an excellent place from which to start.

Google DeepMind has announced its second collaboration with the NHS, working with Moorfields Eye Hospital in east London to build a machine learning system which will eventually be able to recognise sight-threatening conditions from just a digital scan of the eye. The collaboration is the second between the NHS and DeepMind, which is the artificial intelligence research arm of Google, but Deepmind's co-founder, Mustafa Suleyman, says this is the first time the company is embarking purely on medical research. An earlier, ongoing, collaboration, with the Royal Free hospital in north London, is focused on direct patient care, using a smartphone app called Streams to monitor kidney function of patients. The Moorfields collaboration is also the first time DeepMind has used machine learning in a healthcare project. At the heart of the research is the sharing of a million anonymous eye scans, which the DeepMind researchers will use to train an algorithm to better spot the early signs of eye conditions such as wet age-related macular degeneration and diabetic retinopathy.

The US military have launched their first experimental, fully autonomous self-driving warship, dubbed Sea Hunter, and representing a major advance in robotic warfare, which is increasingly forming the core of America's strategy to counter the Chinese and Russians, it's designed to hunt enemy submarines. The 132ft unarmed ASW Continuous Trail Unmanned Vessel (ACTUV) prototype is the naval equivalent of Google's self-driving car. Designed to cruise on the ocean's surface for two or three months at a time and with a range of over 10,000 miles it has neither a crew nor anyone controlling it remotely. And that kind of endurance and autonomy could make it a highly efficient submarine stalker at a fraction of the cost of the Navy's manned vessels. "This is an inflection point," Deputy US Defense Secretary Robert Work said in an interview, adding he hoped such ships might find a place in the western Pacific in as little as five years.

Clifford Chance has become the latest UK law firm to adopt the use of artificial intelligence (AI) after partnering with software provider Kira Systems. The magic circle firm has teamed up with Kira Systems to improve the "speed, efficiency and quality" of its processes. In practice Kira works by searching and analysing text within contracts. The firm believes its use will reduce the time spent carrying out due diligence work and increase the number of documents that can be reviewed. It was chosen by Clifford Chance because the software can be easily modified by the firm's lawyers and is able to adapt to specific client requests.

Chilling aerial footage of Ramadi, a once bustling city in central Iraq, has captured the extent of destruction caused by war. In late December, Iraqi forces, backed by US air strikes, announced the recapturing of Ramadi, which had been lost to the Islamic State of Iraq and the Levant (ISIL, also known as ISIS) group in May 2015. The US-led coalition carried out more than 600 air strikes in the area from July to December last year. A new six-minute clip, released by the International Red Committee of The Red Cross (ICRC) shows homes in Ramadi turned to rubble, along with flattened school, destroyed hospitals and damaged ambulances. READ MORE: Dramatic video'shows destruction of huge ISIL convoy' "Rare aerial footage gathered by ICRC shows the once prosperous Ramadi in central Iraq now in tatters - a ghost town," the ICRC said on Monday.

Air combat veterans proved to be no match for an artificial intelligence developed by Psibernetix. ALPHA has proven to be "the most aggressive, responsive, dynamic and credible AI seen to date."

A treatment regime is a function that maps individual patient information to a recommended treatment, hence explicitly incorporating the heterogeneity in need for treatment across individuals. Patient responses are dichotomous and can be predicted through an unknown relationship that depends on the patient information and the selected treatment. The goal is to find the treatments that lead to the best patient responses on average. Each experiment is expensive, forcing us to learn the most from each experiment. We adopt a Bayesian approach both to incorporate possible prior information and to update our treatment regime continuously as information accrues, with the potential to allow smaller yet more informative trials and for patients to receive better treatment. By formulating the problem as contextual bandits, we introduce a knowledge gradient policy to guide the treatment assignment by maximizing the expected value of information, for which an approximation method is used to overcome computational challenges. We provide a detailed study on how to make sequential medical decisions under uncertainty to reduce health care costs on a real world knee replacement dataset. We use clustering and LASSO to deal with the intrinsic sparsity in health datasets. We show experimentally that even though the problem is sparse, through careful selection of physicians (versus picking them at random), we can significantly improve the success rates.

Biological datasets amenable to applied machine learning are more available today than ever before, yet they lack adequate representation in the Data-for-Good community. Here we present a work in progress case study performing analysis on antimicrobial resistance (AMR) using standard ensemble machine learning techniques and note the successes and pitfalls such work entails. Broadly, applied machine learning (AML) techniques are well suited to AMR, with classification accuracies ranging from mid-90% to low- 80% depending on sample size. Additionally, these techniques prove successful at identifying gene regions known to be associated with the AMR phenotype. We believe that the extensive amount of biological data available, the plethora of problems presented, and the global impact of such work merits the consideration of the Data- for-Good community.

This is the text version of remarks I gave on June 26, 2016, at a panel on the Moral Economy of Tech at the SASE conference in Berkeley. The other panel participants were Kieran Healy, Stuart Russell and AnnaLee Saxenian. We were each asked to speak for ten minutes, to an audience of social scientists. I am only a small minnow in the technology ocean, but since it is my natural habitat, I want to make an effort to describe it to you. As computer programmers, our formative intellectual experience is working with deterministic systems that have been designed by other human beings. These can be very complex, but the complexity is not the kind we find in the natural world.

Pulling these threads together, recently a gaming AI defeated a US Air Force expert combat pilot in simulated combat.