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) …
IBM has come up with a way to use quantum computers to improve machine learning algorithms, even though we don't have anything approaching a quantum computer yet. The tech giant developed and tested a quantum algorithm for machine learning with scientists from Oxford University and MIT, showing how quantum computers will be able to map data at a far more sophisticated level than any classical computer. Somewhat ironically, the testing was done using modelling of only two qubits simulated on a classical computer, because that's the current hardware capability available. There are no quantum computers because qubits can't stay in an entangled state for more than a few hundred microseconds, even in carefully controlled laboratory conditions. They break down into decoherence and can no longer be used to perform calculations in parallel, the feature of quantum computing that will give it awesome processing power.
DevOps combines the information technology and software development teams and increases communication and collaboration between the two groups. With DevOps, then, it becomes possible to adopt an approach to project management that allows for shorter times between new versions of apps or other products. As such, DevOps encourages continual evolution brought about by team or client needs and feedback. Something called process data mining -- analysing large amounts of data about processes and taking action accordingly -- could enhance DevOps practices in several ways. Data mining involves looking through collections of information and identifying patterns.
Earlier this week, Flickr started taking heat across the web after it was specifically mentioned in a report from NBC News that took a deep dive into the'dirty little secret' of using Creative Commons images to help train facial recognition algorithms. The report mentioned multiple datasets used to help companies train machine learning algorithms to better comprehend diversity in facial recognition programs, but one dataset in particular was emphasized and elaborated on: IBM's'Diversity in Faces' set that was derived and iterated upon from more than 100 million Creative Common images gathered by Yahoo and released for research purposes back in 2014. Almost immediately, users around the web started raining down critical comments. Others, such as Flickr's own Don MacAskill, chimed in as well to help clarify the situation. The issue isn't that Flickr is handing over your photos for free to corporations looking to train their artificial intelligence algorithms.
Trial and error is one of the most fundamental learning strategies employed by animals, and we're increasingly using it to teach intelligent machines too. Boosting the flow of ideas between biologists and computer scientists studying the approach could solve mysteries in animal cognition and help develop powerful new algorithms, say researchers. Some of the most exciting recent developments in AI, in particular those coming out of Google DeepMind, have relied heavily on reinforcement learning. This refers to a machine learning approach in which agents learn to use feedback from their environment to choose actions that maximize rewards. Much of the inspiration for the earliest reinforcement learning algorithms came from rules developed to describe the learning behavior of animals, and the deep neural networks more recent approaches rely on also have roots in biology.
Pharmaceutical companies spend a lot of time testing potential drugs, and they end up wasting much of that effort on candidates that don't pan out. Kyle Swanson wants to change that. A master's student in computer science and engineering, Swanson is working on a project that involves feeding a computer information about chemical compounds that have or have not worked as drugs in the past. From this input, the machine "learns" to predict which kinds of new compounds have the most promise as drug candidates, potentially saving money and time otherwise spent on testing. Several prominent companies have already adopted the software as their new model.
I personally love what Victor Frankenstein attempts (although it meets a terrible end) but I must admit how scary this era of technological development is. Humans are forging ahead in the field of artificial intelligence, quickly replacing manpower as we switch from manual to automatic for a number of tasks. Robots are already powering our homes, working in our labs, picking our songs and now, they will be directing our movies. SEE ALSO: This machine writes poetry and it might be better than you at it! Up next, artificial intelligence is all set to take over major roles in filmmaking.
Enterprise security has always been a cat and mouse game, with cyber adversaries constantly evolving their attack systems to get past defenses. Can AI based systems help in warding off new age threats and zero day attacks. To get a perspective, we spoke with Vikas Arora, IBM Cloud and Cognitive Software Leader, IBM India/South Asia, who shares his view on how AI can impact enterprise security. What are your views on the cyber security landscape in India? Which sectors do you think are the most vulnerable today?
In summer 2013, I interviewed for a lead role in the data science and analytics team at tech-for-good company JustGiving. During the interview, I said I planned to deliver batch machine learning, graph analytics and streaming analytics systems, both in-house and in the cloud. A few years later, my former boss Mike Bugembe and I were both presenting at international conferences, winning awards and becoming authors! Here is my story, and what I learnt on the journey -- plus my recommendations for you. I've always been interested in artificial intelligence (AI), machine learning (ML) and natural language processing (NLP).
The Stanford Institute for Human-Centered AI officially launched today. Stanford HAI seeks to become an interdisciplinary global AI hub and to fundamentally change the field of AI by integrating a wide range of disciplines and prioritizing true diversity of thought. Researchers in Korea analyzed literature evaluating 516 AI algorithms for medical image analysis and found that only 6% validated their AI and 0% were ready for clinical use. This lack of appropriate clinical validation is referred to as digital exceptionalism. An analysis of 47 biomedical unicorns found that most of the highest valued startups in healthcare have a limited or non‐existent participation in the publicly available scientific literature.
If one types in "define blockchain" into Google, this is the definition they receive: A system in which a record of transactions made in bitcoin or another cryptocurrency are maintained across several computers that are linked in a peer-to-peer network. Likewise, if one searches for "define artificial intelligence," they receive the following answer: The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. As noted by Kailash Nadh at LiveMint, these two technological advancements aren't related -- and shouldn't be shoehorned together. Examine almost any whitepaper for an initial coin offering (ICO) from 2017 or 2018 and there's a good chance you will find that the project plans on miraculously marrying the blockchain to artificial intelligence in order to not only provide business solutions but also solve all of the world's problems. Throw in some stock images of astronauts and robots, and you've got the complete package.