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.

The Brookings Institution last week published a report from global economy expert Indermit Gill prophesying that the AI leader in 2030 will go on to rule the planet until at least 2100. The territories in the running include the US, China, and the European Union. Economists appear to have reached a general consensus that artificial intelligence is among the four great "general purpose technologies" to come along since the 1800s. Gill argues that AI, like steam power, electricity, and information systems technology, will directly impact the way business is conducted at the global scale by 2030. Related: Here's what AI experts think will happen in 2020 Technological leadership will require big digital investments, rapid business process innovation, and efficient tax and transfer systems.

Artificial Intelligence is a specialized capability involving automated tasks that are performed exclusively by machines driven by thinking ability and logical conclusion; with zero or minimum human intervention. By virtue of AI, machines can solve complex problems, as well as accomplish planning and strategic thinking that were conventionally handled by humans. In order to process large data, AI leverages complex algorithms to initiate the desired action or series of steps to complete a specific task or process in a well-defined domain. It would typically collate data from various touch-points such as IoT and the connected devices to be able to process the same, and is able to achieve the agility to perform the same task even more consistently the next time around. An enterprise tracks its preparedness towards an AI transformation – particularly in terms of infrastructure as well as support for consistent and hassle-free coding in machine languages.

After a draft white paper about the EU's position on AI regulation was leaked earlier this month, Google chief Sundar Pichai, on Monday (20 January), warned the bloc about imposing its own regulations and called for an "international alignment" on the core values of the future laws of the sector. However, ahead of what is expected to be the fourth industrial revolution, the European Commission wants to ensure an "appropriate" ethical and legal framework for the development of AI, which promises to boost innovation while making EU citizens' rights a priority. In November, commission chief Ursula von der Leyen pledged to develop AI legislation similar to the General Data Protection Regulation (GDPR), an EU law on privacy. "It is not about damming up the flow of data, it is about making rules that define how to handle data responsibly," she told MEPs back then. "For us, the protection of a person's digital identity is the overriding priority," she added. According to Ursula Pachl, deputy director-general at the European Consumer Organisation (BEUC), an NGO in Brussels, "it is important that the European Commission has announced a legislative framework and is vocal about the ambition to become a global standard-setter in this area, much like it has done with the GDPR on data protection".

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.

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.

Yes, companies use AI to automate various tasks, while consumers use AI to make their daily routines easier. But governments–and in particular militaries–also have a massive interest in the speed and scale offered by AI. Nation states are already using artificial intelligence to monitor their own citizens, and as the U.K.'s Ministry of Defence (MoD) revealed last week, they'll also be using AI to make decisions related to national security and warfare. The MoD's Defence and Security Accelerator (DASA) has announced the initial injection of £4 million in funding for new projects and startups exploring how to use AI in the context of the British Navy. In particular, the DASA is looking to support AI- and machine learning-based technology that will "revolutionise the way warships make decisions and process thousands of strands of intelligence and data."

Sign up here to receive the Davos Diary, a special daily newsletter that will run from Jan. 20-24. IBM called for rules aimed at eliminating bias in artificial intelligence to ease concerns that the technology relies on data that bakes in past discriminatory practices and could harm women, minorities, the disabled, older Americans and others. As it seeks to define a growing debate in the U.S. and Europe over how to regulate the burgeoning industry, IBM urged industry and governments to jointly develop standards to measure and combat potential discrimination. The Armonk, New York-based company issued policy proposals Tuesday ahead of a Wednesday panel on AI to be led by Chief Executive Officer Ginni Rometty on the sidelines of the World Economic Forum in Davos. The initiative is designed to find a consensus on rules that may be stricter than what industry alone might produce, but that are less stringent than what governments might impose on their own. "It seems pretty clear to us that government regulation of artificial intelligence is the next frontier in tech policy regulation," said Chris Padilla, vice president of government and regulatory affairs at International Business Machines Corp.