New technological advances could have important implications for those affected by disabilities, offering valuable assistance throughout their everyday lives. One key example of this is the guidance that technological tools could provide to the visually impaired (VI), individuals that are either partially or entirely blind. With this in mind, researchers at CloudMinds Technologies Inc., in China, have recently created a new deep learning-powered wearable assistive system for VI individuals. This system, presented in a paper pre-published on arXiv, consists of a wearable terminal, a powerful processor and a smartphone. The wearable terminal has two key components, an RGBD camera and an earphone.

One astronomer had jumped the gun, tweeting ahead of an official announcement by LIGO (the Laser Interferometer Gravitational-Wave Observatory). The observatory had detected an outburst of gravitational waves, or ripples in spacetime, and an orbiting gamma-ray telescope had simultaneously seen electromagnetic radiation emanating from the same region of space. The observations--which were traced back to a colliding pair of neutron stars 130 million light-years away--marked a pivotal moment for multimessenger astronomy, in which celestial events are studied using a wide range of wildly different telescopes and detectors. The promise of multimessenger astronomy is immense: by observing not only in light but also in gravitational waves and elusive particles called neutrinos, all at once, researchers can gain unprecedented views of the inner workings of exploding stars, galactic nuclei and other exotic phenomena. But the challenges are great, too: as observatories get bigger and more sensitive and monitor ever larger volumes of space, multimessenger astronomy could drown in a deluge of data, making it harder for telescopes to respond in real time to unfolding astrophysical events.

This week's announcement of Microsoft's major investment in OpenAI sent hopeful waves throughout the growing industry. OpenAI, co-founded by Elon Musk and Y Combinator chairman Sam Altman in 2015, plans to use the billion-dollar investment to create machine learning that mimics the brain. The 100-employee company specializes in what's referred to as AGI, or Artificial General Intelligence, which may eventually replace or stand in for human behaviour. While the nine figure sum is a testament to tech's commitment in the AI race, it also comes at a time when larger philosophical questions on its development are still awaiting answers. "The announcement is quite significant within the AI industry, and it presents an ambitious goal to deliver on the promise of AGI," said Ben Lamm, CEO of Hypergiant, an Austin-based AI products and services company.

Fans of Douglas Adams's Hitchhiker's Guide to the Galaxy treasure the bit where a group of hyper-dimensional beings demand that a supercomputer tells them the secret to life, the universe and everything. The machine, which has been constructed specifically for this purpose, takes 7.5m years to compute the answer, which famously comes out as 42. The computer helpfully points out that the answer seems meaningless because the beings who instructed it never knew what the question was. Machine-learning may soon enable us to accurately predict how a protein will fold. But it won't be scientific knowledge It's years since I read Adams's wonderful novel, but an article published in Nature last month brought it vividly to mind.

In this research report from AIIM, explore where organizations currently stand in regards to their machine learning and deep learning initiatives. You forgot to provide an Email Address. This email address doesn't appear to be valid. Please provide a Corporate E-mail Address. This email address is already registered.

A DL algorithm is able to learn hidden patterns from the data by itself, combine them together, and build much more efficient decision rules. That's why it can deal with problems that a human brain could not understand - all the value of deep learning is this automatic pattern identification capability. This means handling more complex problems, such as understanding concepts in images, videos, texts, sounds, time series, and all other unstructured data you think of.

Both industries and governments alike have invested significantly in the AI field, with many AI-related startups established in the last 5 years. If another AI winter were to come about many people could lose their jobs, and many startups might have to shut down, as has happened before. Moreover, the economic difference between an approaching winter period or ongoing success is estimated to be at least tens of billions of dollars by 2025, according to McKinsey & Company. This paper does not aim to discuss whether progress in AI is to be desired or not. Instead, the purpose of the discussions and results presented herein is to to inform the reader of how likely progress in AI research is. For a detailed overview of both AI winters check out my first and second medium article on the topic. In this section, the central causes of the AI winters are extracted from the above discussion of previous winters.

The shape of a severe storm, such as this one, is an important factor in whether the storm produces hail and how large the hailstones are, but current hail-prediction techniques are typically not able to take the storm's entire structure into account. NCAR scientists are experimenting with a new machine-learning technique that can process images to weigh the impact of storm shape and potentially improve hail forecasts. This image is freely available for media and nonprofit use.) The same artificial intelligence technique typically used in facial recognition systems could help improve prediction of hailstorms and their severity, according to a new study from the National Center for Atmospheric Research (NCAR). Instead of zeroing in on the features of an individual face, scientists trained a deep learning model called a convolutional neural network to recognize features of individual storms that affect the formation of hail and how large the hailstones will be, both of which are notoriously difficult to predict.

Yes, it might, if humans ask it to. But it is not a good idea to do so as the technology lacks compassion. Armies all over the world are being equipped with AI technologies. For example, the US army3 is keen to build an Advanced Targeting and Lethality Automated System (ATLAS), which will use AI and machine learning to acquire, identify, and engage targets at least 3X faster than the current manual process. However, to stop such machines from turning into killer robots, humans need to control the way in which deep learning algorithms learn. This will require extensive knowledge about what kinds of data will be used to teach machines to fight.

I was blown away by these two incredibly talented data scientists. Nothing inspires me more than having a conversation with people who are literally 10 times smarter than me. We discuss Mat's work on building out patterns for distributed deep learning on Azure. We talk about computer vision, interpretability, robustness, ML engineering and the democratisation of deep learning. Finishing off we discuss where the deep learning space is going in the next 5 years!