Artificially-intelligent medical devices capable of continuously monitoring critically-ill patients and administering treatments when needed, are being investigated in a UK-wide research project. The research network, involving the Universities of Nottingham, Oxford and Warwick, will identify technologies that can provide more personalised and responsive care for cancer and intensive care patients, and those with chronic wounds. The three-year, EPSRC-funded project, which is being led by Prof Stephen Morgan at Nottingham University, will investigate technologies to monitor patients and administer medicines or adjust treatments as necessary, using information from built-in sensors. A particular focus of the research will be on devices that use the type of closed loop control system of feedback and intelligence found in power electronics used to control motors, said Morgan. "These continuously measure and feed their output back into the system, so they can constantly adjust," he said.

Google (NASDAQ: GOOG) is reportedly developing two Android Wear watches that will focus heavily on the Google Assistant, the company's artificial intelligence offering. Citing an unnamed "reliable source," Android Police said the watches, codenamed Angelfish and Swordfish, will be released after the launch of the next Nexus phones, likely in the next several months. One model is said to be larger and support LTE, GPS and heart-rate monitoring, while the other will offer fewer features and lack mobile data. It's unclear whether the gadgets will feature Google's Nexus brand. While the report is unconfirmed, Google is clearly showing a renewed interest in developing its own mobile devices.

Agents have existed already for a long time. In software engineering, they are small distributed applications that demonstrate some form of autonomous behavior. Early examples are monitoring agents, web crawlers, and chat bots. Also, today's trends in distributed and intelligent architectures can be viewed from this existing Agent perspective. Software Agents could be viewed as an evolution of, or an addition to, the (Micro)Services that are underpinning these trends.

Advancements in artificial intelligence, coupled with the proliferation of messaging apps, are fueling the development of chatbots -- software programs that use messaging as the interface through which to carry out any number of tasks, from scheduling a meeting, to reporting weather, to helping users buy a pair of shoes. Foreseeing immense potential, businesses are starting to invest heavily in the burgeoning bot economy. A number of brands and publishers have already deployed bots on messaging and collaboration channels, including HP, 1-800-Flowers, and CNN. While the bot revolution is still in the early phase, many believe 2016 will be the year these conversational interactions take off. In a new report from BI Intelligence, we explore the growing and disruptive bot landscape by investigating what bots are, how businesses are leveraging them, and where they will have the biggest impact.

Twitter just paid 150m for 14-person Magic Pony, a UK-based AI visual search company barely anyone had heard of before the deal. At 10m per employee it marks a high water mark in AI for what is essentially a team acquisition. Magister has tracked 26 AI driven deals since 2014 in the US, Europe and Israel, 11 of which involved companies with less than 50 employees which were acquired largely, or entirely, for the team and capability. Across all 11 deals, the median price paid per employee has reached 2.4m, meaning a high quality AI company with 40 employees would be valued at near 100m. Even if it had little or no revenue.

Software governs much of our daily lives from behind the scenes, from which sorts of information we consume, to who we date. For some, secretive algorithms decide whether they are at risk of committing a future crime. It's only natural to want to understand how these black boxes accomplish all this, especially when it impacts us so directly. Artificial intelligence is getting better all the time. Google, for example, recently used a technique known as deep learning to kick a human's ass at Go, an incredibly complex board game invented thousands of years ago.

For a better approach to make your company smarter, look to APIs. IBM Watson), you can create smarter systems that provide top notch analysis without the need of a data lake. APIs enable data to flow into the machine learning system (since all interactions with the application happens through the APIs, the API traffic represents the totality of all signals about an app's usage). But more importantly, APIs represent a very simple way of taking action. Recommendations can be pushed, for example, as part of the API responses.

What are the best tools to get started with Java machine learning? They've been around for a while, but these days it feels like everyone is talking about artificial intelligence and machine learning. It's no longer a secret reserved to scientists and researchers, with implementations in nearly any new emerging technology. In the following post we'll do a quick overview of the main Java machine learning frameworks, and show how easy it is to get started – without reinventing the wheel and creating your own algorithms from scratch. New Post: How to Get Started with Java Machine Learning https://t.co/ohD3zCCA6j

A new legal technology company called Deftr is today launching a tool powered by artificial intelligence that helps professionals diagram intricate corporate structures. The tool reads text in real time, as it is being typed, then turns that text into a shareable, interactive graphic illustrating a corporate structure or transaction. In a press release being issued today, Deftr's co-founder and CEO Matthew Osman explained: Building charts to represent corporate structures or transactions essentially by hand is a laborious process all too familiar to anyone in corporate life. It's the reason a lot of associates don't have weekends. Our tool automates this outdated, manual process, allowing a user to build a chart in a fraction of the time and focus their efforts on high-level, analytical work. The tool should be of value to law firms, in-house legal departments, accounting firms, financial services and management consultants, Deftr says.

July 6, 2016, Over a third (36%) of all developers who are actively working on Big Data or advanced analytics projects now use elements of machine learning according to Evans Data's recently released Big Data and Advanced Analytics survey report. While the market for machine learning is still fragmented, those developers actively working with machine learning are most likely to be targeting financial sectors, Internet of Things, or manufacturing. The survey of over 500 developers actively working with Big Data also showed that decision trees are the most used analytical model which links in closely with artificial intelligence and machine learning development. Linear regression and logistics regression were the next most cited analytical models. Logistics, distribution, or operations were the company departments most likely to be using advanced data analytics or Big Data solutions.