WIRE)--Virtualitics, LLC, a data analytics in virtual reality (VR) and augmented reality (AR) startup, today announced the launch of its new tool that combines powerful data visualization in VR/AR and artificial intelligence to provide insights and discover actionable knowledge hidden in big and complex data. The company also announced they closed a $3 million investment seed round from angel investors. Virtualitics combines VR/AR with machine learning and natural language in a data exploration, collaborative environment suitable for both data scientists and non-expert users. The technology is the only one of its kind that can provide a simultaneous rendering of up to 10 dimensions, revealing multidimensional relationships present in the data, which may not be discoverable in any other way. The company is based on over a decade of research at Caltech and NASA's Jet Propulsion Laboratory (JPL) and was founded by: professor George Djorgovski, the founding director of Caltech's Center for Data-Driven Discovery; Michael Amori, former Deutsche Bank managing director, Harvard MBA and Caltech alumnus; Dr. Ciro Donalek, computational scientist at Caltech; and Dr. Scott Davidoff, manager of the Human Interfaces Group at NASA's JPL.

WriteLab is a web application with a clear mission: to help you write better. Users upload or copy and paste their writing to the site, and WriteLab analyzes it using a combination of machine learning and natural language processing, then makes suggestions for how to improve the writing sample. WriteLab also comes with built-in analytics and document sharing capabilities, meaning it allows peer and teacher feedback to be integrated into its assessment. WriteLab is unique because it grounds its feedback in pedagogical principles. Instead of simply correcting a writer's mistakes, this web app makes suggestions and gives explanations for each so that the writer can understand the reasoning behind the corrections and decide which ones they want to keep. For instance, when I used the phrase "in recent years" in my writing sample, WriteLab asked, "What does in recent years add to your sentence?"

Increased investor interest in AI startups – from around 10 deals in Q1'11 to over 120 in Q2'16 – can be attributed to recent advances in machine learning algorithms, particularly "deep learning" technology, a souped up version of AI. Just this week, Google integrated deep learning into its Google Translate tool; Baidu announced the launch of DeepBench, an "open source benchmarking tool for evaluating deep learning performance across different hardware platforms"; and NVIDIA introduced Xavier, a deep learning-based supercomputer for driverless cars. In the private market, Google put deep learning in the spotlight back in 2014 when it acquired 4 startups focused on this AI tech in quick succession: DeepMind, Vision Factory, Dark Blue Labs, and DNNresearch. Apple, which joined the race in 2015, most recently acquired Turi, which has developed a deep learning toolkit, among other AI-based solutions. Not to be outdone, Intel has acquired around 5 AI startups since January 2015, including deep learning startup Nervana Systems and, more recently, Movidius.

As investigations into Wells Fargo's systemic banking fraud continue, new findings show that retail branches were given notice of internal audits a day or three in advance of them taking place. This allowed for managers and employees to scrub, purge and modify documents and data, which, by most firsthand accounts, probably looked something like this. With new oversights and practices well underway at Wells Fargo, many banks are having a broader discussion of just what guaranteed transparency and compliance looks like beyond just throwing people and money at the situation. Many institutions are asking themselves, especially in the wake of the Wells Fargo mess, "What does the future of controls, compliance and processes look like?" The answer to this question, like so many others in a rapidly changing, operationally strained industry, is increasingly rooted in the adoption of technologies like automation and artificial intelligence (AI).

HealthTap, the "world's first global health practice," is introducing an artificial intelligence engine to triage cases automatically. Doctor A.I., is a personal AI-powered physician that provides patient with doctor recommended insights. More than a billion people search the web for health information each year, with approximately 10 billion symptom related searches on Google alone. Whilst many resources provide useful information, web search results can only provide content semantically related to symptoms. Dr. A.I. aims to provide more accurate analysis by using what is known as'medical triage', that doctors use to assess what care to provide based on a patient's personal health situation.

There is a long list of possible platforms or libraries that you can use, but here I want to focus on some of the most common. Machine learning platforms are useful because they help reduce the complexity needed to get started. The downside is the lack of flexibility because you need to adapt to their features and options, therefore making it difficult to fit with specific problems in your project. Just as developers use software libraries in their usual work, with deep learning it is no different. A library is simply a set of functions that are pre-made by a development team.

Last September, a U.K. House of Commons committee concluded that it is too soon to regulate artificial intelligence (AI). Its recommendation comes too late: The EU General Data Protection Regulation (GDPR), which comes into force next year, includes a right to obtain an explanation of decisions made by algorithms and a right to opt-out of some algorithmic decisions altogether. These regulations do little to help consumers, but they will slow down the development and use of AI in Europe by holding developers to a standard that is often unnecessary and infeasible. Although the GDPR is designed to address the risk of companies making unfair decisions about individuals using algorithms, its rules will provide little benefit because other laws already protect their interests in this regard. For example, when it comes to a decision to fire a worker, laws already exist to require an explanation, even if AI is not used.

Their machine-learning platform predicted a trio of reagents that was able to generate a never-before-seen cancer-like phenotype in tadpoles. The research, reported in Scientific Reports on January 27, shows how artificial intelligence (AI) can help human researchers in fields such as oncology and regenerative medicine control complex biological systems to reach new and previously unachievable outcomes. The researchers had previously shown that pigment cells (melanocytes) in developing frogs could be converted to a cancer-like, metastatic form by disrupting their normal bioelectric and serotonergic signaling and had used AI to reverse-engineer a model that explained this complex process. However, during these extensive experiments, the biologists observed something remarkable: All the melanocytes in a single frog larva either converted to the cancer-like form or remained completely normal. Conversion of only some of the pigment cells in a single tadpole was never seen; how, the researchers asked, could such an all-or-none coordination of cells across the tadpole body be explained and controlled?

IT seems Samsung (Sammy) will be improving the eye scanner. The more responsive retina eye scanner in both new Galaxy models will take less time to register. With the latest advanced games on smartphones, it is obvious that the market has very high demands from the Mobile Manufacturers also. The speed, processing power, memory, and camera power are obviously increasing in most of the devices, but the major players are focusing on very high-tech features. One of the best additions will be a new, unique glass-like surround.

You can save a massive 91% off the Deep Learning and Artificial Intelligence Introductory Bundle in the Geeky Gadgets Deals store. The Deep Learning and Artificial Intelligence Introductory Bundle normally costs $480 and we have it available for $39. Deep Learning is a set of powerful algorithms that are the force behind self-driving cars, image searching, voice recognition, and many, many more applications we consider decidedly "futuristic." One of the central foundations of deep learning is linear regression; using probability theory to gain deeper insight into the "line of best fit." This is the first step to building machines that, in effect, act like neurons in a neural network as they learn while they're fed more information.