Israeli TV to smartphone audio company Tunity has announced a $12 million Series A financing round from existing investors including former Morgan Stanley CEO John Mack and WeWork founder and CEO Adam Neumann, as well as new investors, including MGM Resorts International. Headquartered in New York and with its development center in Tel Aviv, Tunity has developed deep learning and computer vision based technology to produce an app that allows users to stream and hear live audio from muted televisions directly on their smartphones. Tunity will use the funding to complete the development of its data product, which is already attracting significant interest from television networks and advertisers, as well as to enhance its patented cloud-based Deep Learning and Computer Vision technology. Since its launch, the Tunity app has been downloaded more than 1.5 million times, further proving the company's value proposition to consumers through organic adoption and continued usage. After a user scans a nearby television screen, the Tunity app identifies the live video stream and its exact timing, syncing the audio with the user's mobile device.

The AI (chipsets) market is expected to grow from USD 7.06 Billion in 2018 to USD 59.26 Billion by 2025, at a CAGR of 35.5% from 2018 to 2025. The major drivers for the growth of this market are increasingly large and complex dataset driving the need for AI and the adoption of AI for improving consumer services. Moreover, the increasing number of AI applications, improving computing power, and declining hardware cost are propelling the market growth. In the technology segment, machine learning holds the largest share of the AI (chipsets) market. Moreover, the market for computer vision is expected to grow at the highest CAGR from 2018 to 2025.

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Long Short Term Memory networks, usually called "LSTMs", were introduced by Hochreiter and Schmiduber. These have widely been used for speech recognition, language modeling, sentiment analysis and text prediction. Before going deep into LSTM, we should first understand the need of LSTM which can be explained by the drawback of practical use of Recurrent Neural Network (RNN). So, lets start with RNN. Being human, when we watch a movie, we don't think from scratch every time while understanding any event.

Maximizing intelligence on client devices residing on the edge of the network ensures AI-powered user experiences can be realized with better overall performance and with or without a network connection. The key benefits of on-device AI include real-time responsiveness, improved privacy, and enhanced reliability. Supported within the AI Engine, Snapdragon core hardware architectures – Qualcomm Hexagon Vector Processor, Qualcomm Adreno GPU and Qualcomm Kryo CPU – are engineered to run AI applications quickly and efficiently on-device. This heterogeneous computing approach gives developers and OEMs the ability to optimize AI user experiences on smartphones and other edge devices. "On-device AI demands multiple hardware architectures, software tools, and frameworks to meet developer demand and performance thresholds for the myriad of AI-powered features and apps arriving on smartphones this year," said Gary Brotman, director of product management, Qualcomm Technologies, Inc. "The AI Engine capabilities of select Snapdragon mobile platforms have and will continue to fuel AI innovation among our OEMs and ecosystem partners, and are engineered to accelerate the development and delivery of rich, seamless and compelling features for consumers."

Developers follow a simple dress code but this hasn't pushed them back to help others enrich their shopping experience with Artificial Intelligence. With AI, retailers can go far beyond providing online shopping to better customize the customer's shopping experience. By finding a similar product online to experimenting with your makeup and hair dye from the comfort of your couch, AI provides you with everything. Snap and Shop It happens manier times that you want to purchase a similar product your friend has. But, it becomes very tough to find the same product. You need to perform thorough search.

I thought I'd pose this question because I've been thinking about it a lot lately. Lately, I've had friends and family talk about people who found their boyfriend/girlfriend online. And more and more, these days, that's how people are getting together. I've had some experience with online dating, but not much of it has been good. The more I read about AI, though, the more I wonder how it could be incorporated into the process.

The Adobe Research team has been exploring what the future may hold for selfie photography powered by Adobe Sensei. Great portrait photography requires the right perspective, equipment, and editing expertise. But what happens when we tap into the power of artificial intelligence and deep learning to transform bad portrait shots into good ones – all on a smartphone? By combining perspective effect editing, automatic, software-only photo masking, and photo style transfer technology, we're able to transform a typical selfie into a flattering portrait with a pleasing depth-of-field effect that can also replicate the style of another portrait photo.

A lot of considerations go into building an edtech product. The best edtech products utilize adaptive branching, engage students, and collect learning data. That requires you to understand how the brain learns and retains information, As you construct your product, you're also going to need education leaders to validate the tools they use. Here's what they look for in edtech product validation. You have to understand education For education leaders to endorse your edtech product, they want to know that you understand their business.

According to the latest market research report "Artificial Intelligence Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision), End-User Industry, and Geography - Global Forecast to 2025", published by MarketsandMarkets, the market is expected to grow from USD 21.46 Billion in 2018 to USD 190.61 Billion by 2025, at a CAGR of 36.62% between 2018 and 2025. Major drivers for the market are growing big data, the increasing adoption of cloud-based applications and services, and increasing demand for intelligent virtual assistants. The major restraint for the market is the limited number of AI technology experts. Browse 67 tables and 48 figures spread through 205 pages and in-depth TOC on "Artificial Intelligence Market - Global Forecast to 2025" The market for services is expected to grow at the highest CAGR between 2018 and 2025. The adoption of AI is rapidly increasing in various applications.