New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Until pretty recently, computers were hopeless at producing sentences that actually made sense. But the field of natural-language processing (NLP) has taken huge strides, and machines can now generate convincing passages with the push of a button. These advances have been driven by deep-learning techniques, which pick out statistical patterns in word usage and argument structure from vast troves of text. But a new paper from the Allen Institute of Artificial Intelligence calls attention to something still missing: machines don't really understand what they're writing (or reading). This is a fundamental challenge in the grand pursuit of generalizable AI--but beyond academia, it's relevant for consumers, too.
The lack of deep learning skills is hampering the performance of British businesses, according to new research from operational AI firm Peltarion. Its survey of firms across the UK and Nordic regions found 83 percent of AI decision-makers believe the deep learning skills shortage is affecting their business's ability to compete in the market. Almost half (49 percent) said the shortage is delaying projects, while 44 percent see the shortage as posing a major barrier to further investment in deep learning. The talent shortage is a cause for serious concern among businesses, who see deep learning (a sub-field of artificial intelligence) as an avenue to optimising processes and creating more intelligent data-driven products. As it stands, 71 percent of businesses are actively recruiting in an effort to remedy the skills gap.
Some of the biggest names in artificial intelligence, including two godfathers of the machine learning boom, are betting that clever algorithms are about to transform the abilities of industrial robots. Geoffrey Hinton and Yann LeCun, who shared this year's Turing Prize with Yoshua Bengio for their work on deep learning, are among the AI luminaries who have invested in Covariant.ai, The company, emerging from stealth Wednesday, announced the first commercial installations of its AI-equipped robots: picking boxes and bags of products for a German electronics retailer called Obeta. Picking up everyday boxes and plastic packages might sound trivial, and it is for most humans. Workers in factories and warehouses are frequently given new objects to handle, or a batch of different items mixed together, but it's deceptively difficult for a machine to quickly work out how to grab the next doodad.
CHENNAI: Statistics show that about 40,000 women die in a year because of breast cancer, which is one in every 13 minutes a day. The importance of detecting the disease with the help of Artificial Intelligence is the need of the hour. Cautioning that breast cancer has become the most common disease among women both in the developed and the developing countries in the world in the past few years, Subash Kumar, a Computer Science Engineering graduate with around 12 years of experience in the field of data science, Artificial Intelligence machine learning- deep learning, from Chennai, claims that he has developed an easy way of detecting and curing the same. The application of Artificial Intelligence (AI) machine learning technology with deep learning algorithms to whole-slide pathology images can potentially improve the diagnostic accuracy of breast cancer at a very early stage. Some scholars have assessed the performance of automated deep learning algorithms at detecting metastases in the tissue of women with breast cancer and compared results with pathologists' diagnoses.
The Neural Net of electric vehicle maker Tesla continues to improve regularly, and it seems that the company would like to make sure that it evolves at a faster rate. A new Tesla patent reveals that the company would like to enable a more efficient autonomous driving systems on Tesla vehicles. It would be made possible through a new data pipeline focused on more optimized data processing. The recently spotted Tesla patent titled "Data Pipeline and Deep Learning System for Autonomous Driving" was published just a couple of days ago. The concept is that Tesla would revolutionize and improve beyond deep learning systems used for autonomous vehicles.
Tesla's Neural Net continues to improve and become more advanced on a daily basis, but it appears that the electric car maker is making sure that it will evolve at an even faster rate in the future. A recent patent, for example, would allow Tesla's autonomous driving systems to work more efficiently, thanks to a new data pipeline focused on optimized image processing. Tesla's patent for "Data Pipeline and Deep Learning System for Autonomous Driving" was published on December 26. The idea behind the patent is to revolutionize and improve upon past deep learning systems that have been used for autonomous driving vehicles. In the past, these systems have used "captured sensor data" to retrieve information.
Recent notable applications of deep learning in medicine include automated detection of diabetic retinopathy, classification of skin cancers, and detection of metastatic lymphadenopathy in patients with breast cancer, all of which demonstrated expert level diagnostic accuracy.1,2,3 Recently, a deep-learning model was found to match or outperform human expert radiologists in diagnosing 10 or more pathologies on chest radiographs.4,5 The success of AI in diagnostic imaging has fueled a growing debate6,7,8,9 regarding the future role of radiologists in an era, where deep-learning models are capable of performing important diagnostic tasks autonomously and speculation surrounds whether the comprehensive diagnostic interpretive skillsets of radiologist can be replicated in algorithms. However, AI is also plagued with several disadvantages including biases due to limited training data, lack of cross-population generalizability, and inability of deep-learning models to contextualize.8,10,11,12 Human-in-the-loop (HITL) AI may offer advantages where both radiologists and machine-learning algorithms fall short.13,14
AI is quickly revolutionizing the security camera industry. Several manufacturers sell cameras which use deep learning to detect cars, people, and other events. These smart cameras are generally expensive though, compared to their "dumb" counterparts. The data for the events would then be published to an MQTT topic, along with some metadata such as confidence level. OpenCV is generally how these pipelines start, but [Martin's] camera wouldn't send RTSP images over TCP the way OpenCV requires, only RTSP over UDP.
DeepMind says it has created the first artificial intelligence to reach the top league of one of the most popular esport video games. It says Starcraft 2 had posed a tougher AI challenge than chess and other board games, in part because opponents' pieces were often hidden from view. Publication in the peer-reviewed journal Nature allows the London-based lab to claim a new milestone. But some pro-gamers have mixed feelings about it claiming Grandmaster status. DeepMind - which is owned by Google's parent company Alphabet - said the development of AlphaStar would help it develop other AI tools which should ultimately benefit humanity.
SAN FRANCISCO, Oct. 24, 2019 (GLOBE NEWSWIRE) -- MICRON INSIGHT -- Micron Technology, Inc. (Nasdaq: MU), today announced a powerful new set of high-performance hardware and software tools for deep learning applications with the acquisition of FWDNXT, a software and hardware startup. When combined with advanced Micron memory, FWDNXT's (pronounced "forward next") artificial intelligence (AI) hardware and software technology enables Micron to explore deep learning solutions required for data analytics, particularly in IoT and edge computing. With this acquisition, Micron is integrating compute, memory, tools and software into a comprehensive AI development platform. This platform in turn provides the key building blocks required to explore innovative memory optimized for AI workloads. "FWDNXT is an architecture designed to create fast-time-to-market edge AI solutions through an extremely easy to use software framework with broad modeling support and flexibility," said Micron Executive Vice President and Chief Business Officer Sumit Sadana.