If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Researchers used deep learning to create a new laser-based system that enables imaging around corners in real time. Hiding behind a wall might not be practical for much longer thanks to new technology that uses artificial intelligence to see and even read around corners. A team of researchers from Princeton, Stanford, Rice and Southern Methodist universities devised a system that uses powerful lasers similar to a laser pointer. The beam is bounced off a visible wall and onto a hidden object behind a corner. The beam then bounces off the object and back onto the wall.
The global deep learning market is expected to grow at a CAGR of 51.1% from forecast period 2019 to 2026 and expected to reach the value of around US$ 56,427.2 Deep learning is a subdivision of machine learning in artificial intelligence (AI) concerned with the algorithm inspired by the functioning of human brain termed as artificial neural networks. It is also termed as deep neural learning or deep neural network. Deep learning is evolved with the increasing amount of unstructured data due to digitalization. The available amount of data is utilized in deep learning to process or understand that data for effective decision making in various industry verticals including healthcare, manufacturing, automotive, agriculture, retail, security, human resources, marketing, law, and fintech.
Is it possible to make artificial intelligence more trustworthy by inserting a human being into the decision process of machine learning? It may be, but you don't get something for nothing. That human being better be an individual who knows a lot about what the neural network is trying to figure out. And that presents a conundrum, given that one of the main promises of AI is precisely to find out things humans don't know. It's a conundrum that is sidestepped in a new bit of AI work by scientists at the Technische Universität Darmstadt in Germany.
The world's first self-driving electric-powered ride-sharing vehicle is here, but no word on when you'll actually be able to app-hail this robotaxi. Cruise, the self-driving car division of General Motors, unveiled the Origin on Tuesday night in a former Honda dealership just south of downtown. The six-passenger vehicle looks a bit like a small bus, has no steering wheel or pedals, and offers a cavernous area where two rows of three passengers face each other. In introducing the vehicle, Cruise CEO Dan Ammann, a former president of GM, told a crowd made up mostly of company employees that the Origin "is a production vehicle," adding that an announcement about where and when manufacturing will begin is coming soon. Kyle Vogt, Cruise's co-founder who sold the company to GM in 2016 for $1 billion and now serves as chief technology officer, said that being the first automotive or tech company to introduce a dedicated autonomous ride-sharing car doesn't guarantee success.
Check out what's clicking on Foxnews.com General Motors is looking to "move beyond the car" with a shuttle that can move by itself. The automaker's autonomous vehicle subsidiary, Cruise, unveiled a self-driving shuttle prototype on Tuesday in San Francisco, and it doesn't have a steering wheel, foot pedals or any driver controls -- just seating for six accessed through large sliding doors. The all-electric Origin was designed to provide maximum passenger space and will eventually be deployed in a ride-hailing service run by Cruise. The company originally hoped to launch the service by the end of 2019 but delayed it to further develop the self-driving technology and the infrastructure required to operate a large network of vehicles.
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While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reasonably good familiarity of such details. A solid understanding of the intuition behind such algorithms not only helps in appreciating the logic behind them but also helps in making conscious decisions about their applicability in real life cases. There are several graph based algorithms and most notable are the shortest path algorithms. Algorithms such as Dijkstra's, Bellman Ford, A*, Floyd-Warshall and Johnson's algorithms are commonly encountered. While these algorithms are discussed in many text books and informative resources online, I felt that not many provided visual examples that would otherwise illustrate the processing steps to sufficient granularity enabling easy understanding of the working details.
Deriving more value from analytics and emerging technologies like artificial intelligence starts with trust, simply because data collected for analytics must be trusted. Customers and partners that share data must trust that it's safeguarded and used appropriately from collection through storage and to how it's applied. And once insights emerge from applying analytics to the data, individuals throughout the organization must understand the care given to data management so that they trust those insights -- and use them -- to make decisions and ask new questions. Our global survey of more than 2,400 business leaders and managers provides insight into organizations' activities in each of these key areas and identifies where recognized best practices are becoming more mainstream and where they may still be exceptional. It found that respondents who have advanced their analytics practices to incorporate AI-based technologies such as machine learning and natural language processing work in organizations that do the most to foster data quality, safeguard data assets, and develop cultures of data literacy and innovation.
Machine Learning and data analysis are very important nowadays. Many organizations are are always on the lookout for machine learning and data analysis experts. You can make a lucrative career out of this especially if you invest your time studying the right things. Wccftech is offering a limited time discount offer on the Machine Learning & Data Science Certification Training Bundle. The offer will expire in two days, so avail it as soon as you can.