New Delhi, June 23 The adoption of artificial intelligence (AI) and data utilisation strategy can add $500 billion to India's GDP by 2025, a new Nasscom report showed on Thursday. The AI adoption in four key sectors -- BFSI, consumer packaged goods (CPG) and retail, healthcare, and industrials/automotive -- can contribute 60 per cent of the total $ 500 billion opportunity, according to "AI Adoption Index" Nasscom, EY and Microsoft, EXL and Capgemini. Though the current rate of AI investments in India is growing at a compound annual growth rate (CAGR) of 30.8 per cent and poised to reach $881 million by 2023, it will still represent just 2.5 per cent of the total global AI investments of $340 billion. This creates a massive opportunity for Indian enterprises to accelerate investments and adoption of AI to drive equitable growth across sectors. For India to achieve its $1 trillion GDP goal by FY 2026-2027, it needs to have a strong correlation to the maturity of AI adoption, the report noted.

In recent years, deep learning has been a driving force in advance of artificial intelligence. Deep learning is an approach to artificial intelligence in which a neural network – an interconnected group of simple processing units – is trained with data that are adjusted until it performs a task with maximum efficiency. In this article, we'll talk about deep learning embedded systems and how they can help your organization by improving efficiencies in processes ranging from manufacturing to customer experience. Deep learning is a subfield of machine learning that uses artificial neural networks to simulate how the brain learns. Neural networks are algorithms that use large amounts of data to understand patterns.

The graph represents a network of 1,368 Twitter users whose tweets in the requested range contained "iot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 22 June 2022 at 12:26 UTC. The requested start date was Wednesday, 22 June 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 1-day, 19-hour, 59-minute period from Monday, 20 June 2022 at 04:01 UTC to Wednesday, 22 June 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Ambient.ai is an AI company headquartered in Palo Alto on a mission to prevent as many security incidents as possible. Our breakthrough technology combines cutting-edge deep learning with a contextual knowledge model to achieve human-like perception ability. Ambient's flagship product has been deployed by multiple Fortune 100 companies to solve a mission-critical problem in a way that has never been possible. The company was founded in 2017 by Shikhar Shrestha and Vikesh Khanna who are experts in artificial intelligence from Stanford University who previously built iconic products at Apple, Google, Microsoft, and Dropbox. We are a Series-B company backed by Andreessen Horowitz (a16z), SV Angel, YCombinator, and visionary angels like Jyoti Bansal, Mark Leslie, and Elad Gil.

Surveillance cameras plays an essential role in securing our home or business. These cameras are super affordable. So is setting up a surveillance system. The only difficult and expensive part is the monitoring. For real time monitoring, usually a security personnel or a team has to be assigned. It is simply not feasible for all.

Automakers are jumping into the field of advanced driver assistance systems (ADAS) with both feet, trying to stuff as many features into their new cars as they can. The Insurance Institute for Highway Safety, though, wanted to find out what consumers actually want. The survey shows that the majority of consumers are pretty conservative when it comes to ADAS systems. After surveying 1,000 drivers on three partially automated driving systems (lane centering, automated lane changing, and driver monitoring), the IIHS found that consumers prefer systems where they are more in control that have more safeguards. Although consumer interest in ADAS technologies is strong, they are suspicious the more hands-free the technologies become.

GM's autonomous driving division, Cruise, has begun its paid driverless taxi service in San Francisco and officially took its first fares last night. Cruise has been operating a free driverless taxi service in the area since earlier this year (and got pulled over once), but last night it began charging for this service. Both Cruise and its rival Waymo, a division of Google's parent company Alphabet, have been hoping for some time to start charging for autonomous taxi rides in California. Waymo got permission in February but has not yet started charging fares. Cruise's program is still quite limited, only covering about a third of San Francisco with 30 cars.

The air taxi, much like self-driving cars and delivery drones, is one of those futuristic dreams that seem forever three years away. But recent progress made by leading industry players suggests the concept is finally, slowly, maturing to commercialization. The idea of an urban air taxi is pioneered by Silicon Valley startups that make eVTOLs--electric vertical-takeoff-and-landing vehicles. But compared to electric cars, battery-powered aircraft face bigger challenges on both technical and regulatory fronts, let alone the public's acceptance. On June 21, Archer Aviation, a California-based eVTOL startup traded on the New York Stock Exchange, said it had recently begun testing a prototype called Maker with a new configuration that supports "transition flight"--the transition between an aircraft being lifted by vertical propellers and being carried by the wings for horizontal movement.

CornerNet is a different object detection technique where we detects the objects bounding box by a paired key-points, the top-left corner and the bottom-right corner using a single convolution neural network. By detecting the key points, it eliminates the need of different anchor boxes commonly used in single stage detectors. In this paper by Hei Law and Jia Deng from Princeton University, they have introduced a new approach to object detection which outperforms all the single stage detectors. CornetNet introduces a new type of pooling layer called Corner Pooling, that helps localizing the corners. The Net achieves 42.2% AP on MS COCO dataset.

A chatbot is software that simulates human-like conversations with users via text messages on chat. Its key task is to help users by providing answers to their questions. If we dive deep, chatbots are pieces of conversational software powered by artificial intelligence that have the capability to engage in one-to-one chat with customers on their preferred chat platform such as Facebook Messenger, Whatsapp, Instagram, Telegram, Slack and many more conversational platforms. Chatbots, run by pre-programmed algorithms, natural language processing and/or machine learning and conversed in ways that mimicked human communication. Unlike other automated customer service solutions such as IVRS systems that were universally disliked for their robotic nature, Chatbots are seen to get closer to passing the Turing Test convincingly simulating a human conversational partner so well that it was difficult to sense one was chatting with a machine. British Al pioneer Alan Turing in 1950 proposed a test to determine whether machines could think. According to the Turing test, a computer could demonstrate intelligence if a human interviewer, conversing with an unseen human and an unseen computer, could not tell which was which. Although much work has been done in many of the subgroups that fall under the Al umbrella, critics believe that no computer can truly pass the Turing test.