DEEPMIND, Google's London-based artificial intelligence company, has started training neural networks to recognise the signs of eye disease in medical images. A partnership with Moorfields Eye Hospital in London has given the company access to about a million anonymised retinal scans, which DeepMind will feed into its artificial intelligence software. The project will target two of the most common eye diseases – age-related macular degeneration and diabetic retinopathy. More than 100 million people around the world have these conditions. Moorfields is providing scans of the back of people's eyes, as well as more detailed scans known as optical coherence tomography (OCT). The idea is that the images will let DeepMind's neural networks learn to recognise subtle signs of degenerating eye conditions that even trained clinicians have trouble spotting.

"Flint seeks to invest in ground-breaking technologies that directly improve human life," said Flint Capital Partner Artem Burachenok. "Findo's ability to mitigate the growing challenge of massive data in everyone's inbox, cloud and storage solution is a game changer." Findo's smart search engine helps users quickly find information buried in a wide array of documents, slides, audio files, images, or any other information from sources as varied as Dropbox, Google Drive, Evernote and Gmail, Exchange and Outlook. Findo search bots can search from Slack, Telegram, Facebook Messenger and Skype and deliver results right to the messenger. "We are excited to have Flint as one of our investors," said Gary A. Fowler, Co-Founder and CEO of Findo.

Notably, one of the big differences between machine learning and computer-assisted analysis (where humans are involved) is that the recent breakthroughs in machine learning enable computers to teach themselves how to solve problems. Now, machine learning enables computers to find answers in ways that are unguided by human intervention. Other examples include Google's self-driving car, how Netflix suggests which movies you should try next, and how a dating site suggests which people are most likely to be a suitable match for you… Unexpected insights As with most technological tools today, almost any company or sector can leverage machine learning to better serve their customers. Companies that are open to innovative ways of finding insights in their data can ultimately serve their customers more efficiently and even develop closer relationships with them in the long-term.

It is no surprise that data analytics and machine learning are fast becoming key components of every innovative company's toolkit, given the massive increase in the amount of data that companies are generating. Because of the sheer volume and complexity of data being created, it is often beyond human capacity to find relevant trends or insights within what has been tagged as'Big Data'. Notably, one of the big differences between machine learning and computer-assisted analysis (where humans are involved) is that the recent breakthroughs in machine learning enable computers to teach themselves how to solve problems. So previously, when humans were directing computers, they were limited to very direct questions and answers (for example, "what is my top selling item?") and required the person using the machine to dictate which method to use to the solve the problem. Now, machine learning enables computers to find answers in ways that are unguided by human intervention.

It's no secret, artificial intelligence (AI) is capable of doing unimaginable things, however understanding how human behave is not one of those, but a team at MIT's Computer Science and Artificial Intelligence Laboratory is keen on changing that. Basically, researchers turned computers into sofa spuds by dishing out 600 hours of footage from some of the most popular TV shows including "Desperate Housewives," "Scrubs" and "The Office," NPR reported. Every clip was made to conclude with either one of the four actions: a hug, a kiss, a high five or a handshake. The computer was then challenged to predict which of the four aforementioned actions was about to happen. With the aid of learning algorithm, the artificially intelligent test subjects successfully predicted the appropriate action 43 percent of the time, which is well below the 71 percent success rate from human test subjects, CNet reported.

Artificial intelligence (AI) assistants, like Apple's Siri and Microsoft's Cortana, can help internet users with a variety of activities, especially when they're on the go. According to June 2016 research, nearly two-thirds of AI users in the US use these personal assistants to answer general questions. San Francisco, CA–based AYTM Market Research surveyed 1,000 US internet users about their dealings with an AI assistant. Overall, 58% had never used one, while about a quarter used AI assistants on at least a monthly basis. According to the survey, the majority (64.5%) of AI users said they used AI assistants to ask general questions, followed by getting directions while driving (39.7%) and making calls (25.2%). Other research confirms, though, that few mobile phone owners in the US actually use a voice-controlled personal assistant regularly.

WASHINGTON – Congress passed an aviation bill Wednesday that attempts to close gaps in airport security and shorten screening lines, but leaves thornier issues unresolved. The bill also extends the Federal Aviation Administration's programs for 14 months at current funding levels. It was approved in the Senate by a vote of 89 to 4. The House had passed the measure earlier in the week and it now goes to President Barack Obama, who must sign the bill by Friday when the FAA's current operating authority expires to avoid a partial agency shutdown. Responding to attacks by violent extremists associated with the Islamic State group on airports in Brussels and Istanbul, the bill includes an array of provisions aimed at protecting "soft targets" outside security perimeters. Other provisions designed to address potential "insider threats" would toughen vetting of airport workers and other employees with access to secure areas, expand random employee inspections and require reviews of perimeter security.

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This entry was posted in Artificial Intelligence, Machine Learning and tagged AI, Algorithms, Machine Learning.

Chat bots have ballooned in popularity in recent months, and now we're seeing some interesting examples of how that technology, where computers interact and respond to human requests, is being used to solve other problems. Today, Zendesk is taking the wraps off "Automatic Answers", a service for businesses to reply to emails from customers without ever having a human employee get involved. Automatic Answers is not your average, run-of-the mill email autoresponder. The service was built using a machine learning platform that Zendesk's in-house teams of data scientists and engineers, which are based out of Melbourne, Australia, have been developing on for a while now. That machine learning platform was first announced last year and it also powers a service Zendesk announced last October, Satisfaction Prediction, which is able to monitor customer-company interactions to -- as its name implies -- determine whether the customer is getting what she or he needs. The machine learning/AI element means that the responses in Automatic Answers are not only reading and responding specifically to what you the customer is asking, but it is technically getting smarter with each response (and presumably using a bit of Satisfaction Prediction to figure out if it's getting it right).