Artificial Intelligence (AI) is a growing topic in the technology and business world. Perhaps more than our personal lives, AI is entering the business world more and more each day. OneReach, an omnichannel automation service, rounded up 10 of the most interesting stats about AI today, and its future. There was more than 300 million in venture capital invested in AI startups in 2014, a 300% increase over the year before. By the end of 2018, "customer digital assistants" will recognize customers by face and voice across channels and partners.

Tech and the elderly are often thought of as being incompatible. Everyone has spent painful hours teaching their grandmother how to text, and entire days have been dedicated to setting up a computer for an elderly person. But tech can actually help the elderly in numerous ways. From smart homes to wireless trackers, tech for the elderly can increase independence, alleviate the concerns of carers and relatives, and generally make life a whole lot easier. The elderly tech market should not be underestimated: the proportion of Britons aged 85 and over is expected to double from 2.5% to 5% within 20 years.

An entrepreneur has to be a chameleon in different fields and always be up-to-date on the latest news. When we say artificial intelligence, you easily think that this has nothing to do with you, but in fact this has everything to do with you.

Umbo CV has raised a 2.8 million seed round for its security cameras, which use artificial intelligence to identify suspicious activity and prevent crimes before they happen. The Taipei- and San Francisco-based startup's funding was led by AppWorks Ventures, with participation from Mesh Ventures, Wistron Corporation, and Phison Electronics. The two-year-old startup has already shipped its system--including cameras and a cloud-based management platform--to clients in Dubai, the United States, and Europe, and will begin mass manufacturing next month. Co-founder and chief executive officer Shawn Guan says Umbo CV has also received 1.4 million in pre-orders and letters of intent from other customers. Its funding will be used for research and hiring.

A team of researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) led by Li-Shiuan Peh has come up with a new infrared depth-sensing system. The new system, which works outdoors as well as in, was built by attaching a US 10 laser to a smartphone, with MIT saying the inexpensive approach could be used to convert conventional personal vehicles, such as wheelchairs and golf carts, into autonomous ones. Inexpensive rangefinding devices, such as the Microsoft Kinect, have been a great help to robotics engineers. Using the off-the-shelf product that relies on an infrared laser to measure distance, they allow for rapid prototyping and the ability to create robots that can sense and navigate in their environments without having to constantly reinvent the necessary technology. Unfortunately, Kinect and similar infrared-based systems tend to be a bit fussy when it comes to ambient light conditions.

Years ago I had coffee with a friend who ran a startup. He had just turned 40. His father was ill, his back was sore, and he found himself overwhelmed by life. "Don't laugh at me," he said, "but I was counting on the singularity." My friend worked in technology; he'd seen the changes that faster microprocessors and networks had wrought. It wasn't that much of a step for him to believe that before he was beset by middle age, the intelligence of machines would exceed that of humans--a moment that futurists call the singularity.

An amputee has said he has paid nearly 10,000 on a prosthetic limb because he was not told he could have been entitled to a similar one on the NHS. Ivor Sault, 69, from Devizes, Wiltshire, found a leg provided by the NHS was painful so decided to pay privately for a different type. Mr Sault has since discovered he could have got the replacement on the NHS. The NHS said a request for an alternative could have been considered if it had been aware he was unhappy. Mr Sault, who lost his leg in motorbike accident over 50 years ago, said a previous prosthetic limb, which he was happy with, wore out.

PALO ALTO, Calif., March 10, 2016 -- Hewlett Packard Enterprise (NYSE: HPE) today announced a new version ofHPE IDOL. This latest offering applies data analytics and machine learning for organizations to automate and supplement a vast array of manual-based tasks such as trend analysis and video surveillance. HPE IDOL 11 combines context-based analytics and visualization capabilities to empower knowledge workers and help companies take a data-driven approach to their processes, operations, and customer interactions. "Companies across the globe are embracing the power of analytics in everything they do," said Fernando Lucini (@fernandolucini), CTO, Big Data, Hewlett Packard Enterprise. "Traditional databases were never designed to analyze human information and often lack the key capabilities necessary to effectively and reliably understand unstructured data. With the addition of next generation artificial intelligence, neural networks and machine learning based capabilities we now enable organizations to harness 100% of their data."

Cloud Machine Learning from Google, a subsidiary of Alphabet Inc (NASDAQ: GOOGL), will emerge as a best-in-class offering as enterprises look to harness machine learning for their own use, according to Eric Sheridan of UBS. Cloud Machine Learning, which was launched at the recent Google Cloud Next event, is a machine learning platform based on the same underlying technology that powers Google Now and Google Photos. "Google will make machine learning a focal point of GCP (Google Cloud Platform) –a differentiating feature set in our view," Sheridan wrote in a note to clients. Google also unveiled Google Stackdriver, a new tool for monitoring/logging/alerting across multiple cloud services. Currently, it supports AWS and GCP, while support for private cloud is expected in the coming days.

Current evolutionary theory describes a Darwinian machine – i.e., heritable variation in reproductive success that assumes fixed mechanisms of variation and selection operating on a fixed reproductive unit. But, in fact, none of these mechanisms is fixed in nature. For example, the distribution of phenotypic variation changes over evolutionary time as a result of the evolution of development, the selective pressures on traits change as a result of the evolution of ecological interactions, and even the identity of the evolutionary unit changes as a result of the evolution of new reproductive strategies and new mechanisms of inheritance. The circular causality implied by an evolutionary process that alters its own mechanisms results in conceptual difficulties and controversies in many areas of evolutionary biology. However, in computer science, the idea that an algorithmic process can improve over time as a function of past experience, including its own past behaviour, has been thoroughly studied in the field of machine learning.