neural network


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 rise of artificial intelligence comes with rising needs for power

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Advances in technology can allow you to order food by voice or unlock your phone with your face, but those new capabilities could take a toll on the environment. Enhanced tech capabilities are being developed through the use of artificial-intelligence approaches like neural networks, which detect patterns in speech and images by training programs across countless data points. That process constantly crunches reams of information on power-hungry servers in data centers that use a substantial amount of energy to power, cool and monitor the servers. The result: Training a neural network can emit 17 times more carbon dioxide than an average American does in a year, and five times the lifetime emissions of an average car. Those are the findings of a recent paper by researchers at the University of Massachusetts, Amherst, which highlighted the substantial power generated by AI technologies.


Language, trees, and geometry in neural networks

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Left image in each pair, a traditional parse tree view, but the vertical length of each branch represents embedding distance. Right images: PCA projection of context embeddings, where color shows deviation from expected distance.


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.


To Power A.I., Start-Up Creates a Giant Computer Chip

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Some experts believe these chips will play a key role in the race to create artificial intelligence, potentially shifting the balance of power among tech companies and even nations. They could feed the creation of commercial products and government technologies, including surveillance systems and autonomous weapons. Google has already built such a chip and uses it in a wide range of A.I. projects, including the Google Assistant, which recognizes voice commands on Android phones, and Google Translate, which translates one language into another. "There is monstrous growth in this field," said Cerebras's chief executive and founder, Andrew Feldman, a chip industry veteran who previously sold a company to the chip giant AMD. New A.I. systems rely on neural networks.


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.


How Artificial Intelligence Is Transforming Digital Marketing

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When you imagine what artificial intelligence (AI) "looks like," you might be thinking of a synthetic consciousness developed by humans, as many sci-fi movies portray. Apart from the realm of fantasy, AI is simply a system that can perform tasks that normally requires human intelligence. These include problem-solving, recognizing emotions, and even diagnosing diseases. AI marketing is a method of leveraging technology to improve the customer journey. It can also be used to boost the return on investment (ROI) of marketing campaigns.