Driving increased "data gravity" means customers are less likely to divert new data workloads to alternative platforms, and SalesForce's two recent acquisitions – MuleSoft (a data integration and transformation company) for $6.5 billion in March (https://rob.al/2MgoXEa) and Datorama (cloud AI for marketing) for $800 million (https://rob.al/2Me5qo8) further highlight their desire to make it as easy as possible for customers to get data on to their platform and process it there.
The relationship management software company has announced its fourth acquisition this year.
Recent advances in AI mean that it would now be possible to develop autonomous weapons – systems which are capable of making automated decisions to take human life. Elon Musk, DeepMind and others have signed a pledge never to create AI weapons:
"We the undersigned agree that the decision to take a human life should never be delegated to a machine. It goes on to warn "lethal autonomous weapons, selecting and engaging targets without human intervention, would be dangerously destabilizing for every country and individual."
The signatories have promised to “neither participate in nor support the development, manufacture, trade, or use of lethal autonomous weapons.”
A big step towards making devices like Alexa or Siri (or more likely Google Assistant, given that this research comes from DeepMind) better understand humans is the development of "theory of mind". By around 4 years old, human children can understand that their beliefs may diverge from those of others, and that understanding that divergence can help predict likely future behaviour of others – applying this to human interaction might make the experience more natural. A first step, DeepMind's algorithm is able to analyse the behaviour of AI systems which are otherwise too complex for people to understand and to try to predict their behaviour.
Algorithms achieve a machine theory of mind
Among many promising applications for AI in medicine, cardiology is one with the greatest potential. Current clinical practice calls for manual analysis of a wide range of complex diagnostic imagery, and this analysis is time consuming and error prone, even for the most experienced doctors. Advances in the technology are hampered by ethical concerns that augmenting human decisions with AI violates the relationship between doctor and patient.
The continuous development of the technological sector has enabled the industry to merge with medicine in order to create new integrated, reliable, and efficient methods of providing quality health…
Following the controversy around (and expiration of) Google's $15 million contract with the US Department of Defense (https://rob.al/2M0XY2z), the US government has now signed an $885 million contract with Booz Allen – and i'm willing to bet that there will be far less public debate around the ethics of the applications of technology under this contract than with Google.
The U.S. Department of Defense will for the first time be using large-scale AI systems that could automate mundane tasks and augment the work of military members. The contract also will go toward…
Yet another example of the value of simulated training, researchers at OpenAI trained a robotic hand using hundreds of years of object manipulation inside a computer simulation. The resulting hand movements are significantly more dexterous than anything developed so far.
A reinforcement-learning algorithm allows Dactyl to learn physical tasks by practicing them in a virtual-reality environment.
In an interesting piece of research intended to highlight the biases embedded in AI systems and training data, a team at the University of Melbourne produced a system capable of making assessments of physical attributes and the emotional state of people in a photo. Hidden in the assessments are a range of biases – gender is assumed to be a binary state, there are only five ethnicities, and emotional judgement or "responsibilities" are clearly highly subjective.
Would you be freaked out if a facial recognition mirror started making judgments about your age, gender, race, attractiveness, and even trustworthiness? Get ready to meet the Biometric Mirror, a…
Two years after the unveiling of the original Tensor Processing Unit, Google's announced "Edge TPU" – tiny AI accelerator chips designed to be embedded in IoT devices. Models will be trained on large, server clusters like today, and then embedded in edge devices in factories or workshops – perhaps analysing samples for quality control, processing movement of objects around a plant, etc.
The hardware is destined for enterprise applications, not your phone
In a major breakthrough, researchers from Deep Mind have improved the accuracy of AI lip reading, with half the error rate of previous methods, and over 50% accuracy. If embedded in to smart devices, it could make lip reading accessible to anyone with a smartphone – both helping the many people who struggle in noisy and crowded environments, and putting at risk the privacy of many others.
A new algorithm silences its competition
One of the questions we often ask in interviews is for a candidate to talk about a piece of data they've seen recently which surprised or interested them – an interest in data journalism is (for my team) an indicator of a person interested in numbers, a prerequisite for a successful career in data and analytics. These sorts of data are going to become more accessible as Google News starts to include them in search results to help quash "fake news"
Google is shining its Search spotlight on useful news data.