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) …
From faster and cheaper drug trials to fully "conscious" cities, digital replicas are changing the face and pace of innovation. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Last year the world held its breath as Notre Dame Cathedral stood shrouded in flames. After the fire was extinguished, and it was revealed that the iconic cathedral was not lost, the hard work of restoration began. Until very recently, that process would have begun with a search through dusty archival blueprints to guide the intricate repair works.
Automated machine learning is one of the trendiest and most popular areas of enterprise AI software right now. With vendors offering everything from individual automated machine learning tools to cloud-based, full-service programs, autoML is quickly helping enterprises streamline business process and dive into AI. In light of the rise of autoML, analysts and experts are encouraging enterprises to evaluate their specific needs alongside the intended purpose of the tools -- to augment data scientists' work -- instead of trying to use autoML without a larger AI framework. Whether your enterprise has a flourishing data science team, citizen data science team or relies heavily on outsourcing data science work, autoML can provide value if you choose tools and use cases wisely. Enterprises are applying automated machine learning in a diverse range of use cases, from developing retail insights to training robots.
Cybercriminals are always evolving their efforts and coming up with more advanced ways to target their victims. And while there are many tools available to stop them, there is a lot of space for improvement. Especially if you take automation into account. Machine learning and artificial intelligence are playing a significant role in cybersecurity. Automation tools can prevent, detect, and deal with tons of cyber threats way more efficiently and faster than humans.
One of the prime challenges of a language-based AI model is to understand the context of the surrounding content. To solve this problem, Google has introduced a new model called Reformer, which understands the context of 1 million lines using just 16GB space. The company built this to solve problems of its old model Transformer -- a neural network that compares words in a paragraph to each other to understand the relationship between them. Early birds are even cooler. However, as it uses pair matching, Transformer takes a lot of data space if it needs to process text more than a few thousand words.
Data science jobs are one of the highest paying jobs of this decade. The democratization of analytics tools along with the rise in reading resources has drawn more attention towards this thriving sector. In India, data science jobs are on a rise as every company from startup to industry leaders are incorporating algorithmic solutions into their workflows. In this article, we bring you top 10 data science jobs in Bengaluru -- the Silicon Valley of India. This job is for those who like to write smart algorithms and deal with complex problems that require a mix of AI/ML, big data, computer vision, NLP and a dash of probability and statistics to solve.
Hundreds of law enforcement agencies across the US have started using a new facial recognition system from Clearview AI, a new investigation by The New York Times has revealed. The database is made up of billions of images scraped from millions of sites including Facebook, YouTube, and Venmo. The Times says that Clearview AI's work could "end privacy as we know it," and the piece is well worth a read in its entirety. The use of facial recognition systems by police is already a growing concern, but the scale of Clearview AI's database, not to mention the methods it used to assemble it, is particularly troubling. The Clearview system is built upon a database of over three billion images scraped from the internet, a process which may have violated websites' terms of service.
Google's chief executive called Monday for a balanced approach to regulating artificial intelligence, telling a European audience that the technology brings benefits but also "negative consequences." Sundar Pichai's comments come as lawmakers and governments seriously consider placing limits on how artificial intelligence is used. "There is no question in my mind that artificial intelligence needs to be regulated. The question is how best to approach this," Pichai said, according to a transcript of his speech at a Brussels think tank. He said there's an important role for governments to play and that as the European Union and the U.S. start drawing up their own approaches to regulation, "international alignment" of any eventual rules will be critical.
Developed at the Artificial Intelligence Center of the Stanford Research Institute (SRI) from 1966 to 1972, SHAKEY was the world's first mobile intelligent robot. According to the 2017 IEEE Milestone citation, it "could perceive its surroundings, infer implicit facts from explicit ones, create plans, recover from errors in plan execution, and communicate using ordinary English. SHAKEY's software architecture, computer vision, and methods for navigation and planning proved seminal in robotics and in the design of web servers, automobiles, factories, video games, and Mars rovers." In November 1963, Charles Rosen, head of the AI group at SRI, wrote a memo in which "he proposed development of a mobile'automaton' that would combine the pattern-recognition and memory capabilities of neural networks with higher-level AI programs," according to Nils Nilsson in his book The Quest for Artificial Intelligence. In April 1964, SRI submitted to the Advanced Research Projects Agency (ARPA) at the U.S. Department of Defense, a proposal for research in "Intelligent Automata," which it claimed would ultimately lead to "the development of machines that will perform tasks that are presently considered to require human intelligence."