Neural Networks


Deep Learning Chatbots: Everything You Need to Know

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When you're creating a chatbot, your goal should be to make one that it requires minimal or no human interference. This can be achieved by two methods. With the first method, the customer service team receives suggestions from AI to improve customer service methods. The second method involves a deep learning chatbot, which handles all of the conversations itself and removes the need for a customer service team. Such is the power of chatbots that the number of chatbots on Facebook Messenger increased from 100K to 300K within just 1 year.



The hottest startups in Tel Aviv

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Tel Aviv is the city with the highest number of startups per capita in the world, according to the 2018 Global Startup Ecosystem report -- more than 6,000, of which 18 are unicorns. The city's tech cluster, dubbed Silicon Wadi, is home to more than 100 venture capital funds, plus hundreds of accelerators and co-working places. "Tel Aviv is transitioning from startup nation to scale-up nation," says Eyal Gura, co-founder of Zebra Medical Vision. Amit Gilon, an investor at Kaedan Capital VC fund, agrees – adding that Israel is not just about successful B2B companies anymore, such as Checkpoint, Nice and Amdocs, but also about "big B2C success stories like Playtika, Wix, Fiverr and others". Founded in 2015, Arbe has built a 4D ultra-high-resolution imaging radar for cars.


How AI Accelerators Are Changing The Face Of Edge Computing

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AI has become the key driver for the adoption of edge computing. Originally, the edge computing layer was meant to deliver local compute, storage, and processing capabilities to IoT deployments. Sensitive data that cannot be sent to the cloud for processing and analysis is handled by the edge. It also reduces the latency involved in the roundtrip to the cloud. Most of the business logic that runs in the cloud is moving to the edge to provide low-latency, faster response time.


Creating a neural network from scratch in JavaScript -- Part 1.2

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In this part, we're going to create a simple example of how we can use the neuron.js Throughout this series, we will try to describe how to use this code to build neural networks, AI bots, and potentially our own deep learning framework. There are many great resources on the fundamentals of neural networks, how they work, why they work, but there are few articles on how to actually build neural networks from a software development perspective. With this very "technical" series, I hope to address this; for a quick introduction, checkout this simple introduction to neural networks, Eric Elliot's article on neurons, or 3Blue1Brown's video series on neural networks. In this example we will be teaching a neural network created with the neuron.js


Machine learning for sensors

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Today microcontrollers can be found in almost any technical device, from washing machines to blood pressure meters and wearables. Researchers at the Fraunhofer Institute for Microelectronic Circuits and Systems IMS have developed AIfES, an artificial intelligence (AI) concept for microcontrollers and sensors that contains a completely configurable artificial neural network. AIfES is a platform-independent machine learning library which can be used to realize self-learning microelectronics requiring no connection to a cloud or to high-performance computers. The sensor-related AI system recognizes handwriting and gestures, enabling for example gesture control of input when the library is running on a wearable. A wide variety of software solutions currently exist for machine learning, but as a rule they are only available for the PC and are based on the programming language Python.


New brain map could improve AI algorithms for machine vision

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IMAGE: By analyzing digital images of marmoset brains injected with neuronal tracers (indicated by the arrows), Cold Spring Harbor Laboratory researchers discovered that the primate's visual system worked differently than previously... view more Despite years of research, the brain still contains broad areas of unchartered territory. A team of scientists, led by neuroscientists from Cold Spring Harbor Laboratory and University of Sydney, recently found new evidence revising the traditional view of the primate brain's visual system organization using data from marmosets. This remapping of the brain could serve as a future reference for understanding how the highly complex visual system works, and potentially influence the design of artificial neural networks for machine vision. In the quest of the whole-brain connectivity in marmosets, the team found that parts of the primate visual system may work differently than previously thought. Mapping out how distinct types of cells connect can help researchers understand how groups of cells play in concert to relay and process sensory information from the outside environment to the brain.


Knowledge-Powered Deep Learning for Word Embedding - Semantic Scholar

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The basis of applying deep learning to solve natural language processing tasks is to obtain high-quality distributed representations of words, i.e., word embeddings, from large amounts of text data. However, text itself usually contains incomplete and ambiguous information, which makes necessity to leverage extra knowledge to understand it.


The Long Game of Research

Communications of the ACM

The Institute for the Future (IFTF) in Palo Alto, CA, is a U.S.-based think tank. It was established in 1968 as a spin-off from the RAND Corporation to help organizations plan for the long-term future. Roy Amara, who passed away in 2007, was IFTF's president from 1971 until 1990. Amara is best known for coining Amara's Law on the effect of technology: "We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run." This law is best illustrated by the Gartner Hype Cycle,a characterized by the "peak of inflated expectations," followed by the "trough of disillusionment," then the "slope of enlightenment," and, finally, the "plateau of productivity."


As Search Engines Increasingly Turn To AI They Are Harming Search

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For more than half a century our digital search engines have relied upon the humble keyword. Yet over the past few years, search engines of all kinds have increasingly turned to deep learning-powered categorization and recommendation algorithms to augment and slowly replace the traditional keyword search. Behavioral and interest-based personalization has further eroded the impact of keyword searches, meaning that if ten people all search for the same thing, they may all get different results. As search engines depreciate traditional raw "search" in favor of AI-assisted navigation, the concept of informational access is being harmed and our digital world is being redefined by the limitations of today's AI. At first glance, the evolution of search from simple TF-IDF keyword queries into today's AI-powered personalized digital navigation is a positive step towards making the digital world more accessible to the general public.