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) …
Below is a list of seven open-source platforms that help businesses integrate machine learning into their production process. With these toolkits, businesses, regardless of their size, can get access to the same ML resources developed and used by prestigious companies. In 2015, Amazon's subsidiary AWS (Amazon Web Services) launched Amazon Machine Learning as part of its Cloud-based solutions. AML is a deliberately simplified platform intended for developers of any skill level to walk them through the creation of machine learning predictive models. Google uses TensorFlow toolkit for its own products and services.
An AI algorithm developed by researchers at Salesforce generates snippets of text that describe the essence of long text. These tools can help writers skim through a lot of articles and find relevant topics to write about. "Since new semantic technologies are now mature enough to read human language, journalists and professional writers can finally go back to writing for people," Cuofano says. "The next revolution (which is already coming) is the leap from NLP to a subset of it called NLU (Natural Language Understanding)," Cuofano says.
Artificial intelligence doesn't have to be super-sophisticated to make a difference in people's lives, according to a new Yale University study. Even "dumb AI" can help human groups. In a series of experiments using teams of human players and robotic AI players, the inclusion of "bots" boosted the performance of human groups and the individual players, researchers found. The study appears in the May 18 edition of the journal Nature. "Much of the current conversation about artificial intelligence has to do with whether AI is a substitute for human beings.
I have written on Artificial Intelligence (AI) before. Back then I focused on the technology side of it: what is part of an AI system and what isn't. But there is another question which might be even more important. What are we DOING with AI? Part of my job is to help investors with their due diligence. I discuss companies with them in which they might want to invest.
Early detection of cancer: Developing NLP classifiers to analyze biomedical literature microRNAs are bio-markers, which may indicate cancer and other diseases even at early stage. Together, we developed a pipeline and an NLP classifier to detect relations between genes and micro-RNAs in medical research documents. The generalized code and leanings are open sourced and shared on Github. Sound and vision: Visual anomalies from audio data using deep learning We walk you through an effort by Sierra Systems with help from Microsoft and the Microsoft Cognitive Toolkit to detect and classify oil pipeline leaks, using audio data from a sensor ball deployed by Pure Technologies. We convert this audio data into images and use state-of-the-art Deep Learning techniques in the realm of image recognition to find'visual' anomalies and tell leaks from everyday events.
On Thursday 4 May 2017, Hogan Lovells' Tech Hub hosted Azeem Azhar, renowned strategist, product entrepreneur and writer, who spoke about the current status and implications of Artificial Intelligence ("AI"). Far from being a futuristic ambition, we are living in a world where countless daily activities are powered by AI. Whilst Artificial General Intelligence may yet be a few years away, we are seeing applications of Artificial Narrow Intelligence across a range of use cases from virtual personal assistants to news generation, personalised content and movie recommendations. As Azeem outlined, this penetration of AI over the past few years has been growing exponentially. This has been enabled by the combination of progress in three critical areas, each of which supports and feeds into the other – processing power, availability of data and innovations in machine learning.
Even more concerning, researchers have shown that completely random nonsense images can be misclassified by CNNs with very high confidence as objects recognizable to humans, even though a human would clearly recognize that there was no image there at all (e.g. If those system observations are intentionally tainted with noise designed to defeat the CNN recognition, the system will be trained to make incorrect conclusions about whether a malevolent intrusion is occurring. Adversarial Machine Learning is an emerging area in deep neural net (DNN) research. The current state of AI has advanced to general image, text, and speech recognition, and tasks like steering the car or winning a game of chess.
Uber employee Anthony Levandowski is at the center of the ride-sharing service's current legal battle with competitor Waymo. But now, Levandowski has another potential adversary against him: his own company. Uber said in a letter to Levandowski that if he did not follow the court's orders, the company could take action that could include firing him, the New York Times reports. Via TechCrunch, the letter, which was sent May 15, requested that Levandowski either confirm he never took documents from Waymo or that he hand over any documents within his possession. Levandowski, who is not directly being sued, has so far claimed his Fifth Amendment rights against self-incrimination during the case.
Blood is one of the most vital components of the human body, but we can't create an artificial version, which means we must rely on donations for transfusions and operations. However, two recent studies suggest we're closer than ever to making a limitless, articifial supply of blood. In one new study, researchers created a mix of different types of blood stem cells that produced different kinds of human blood cells when transfused into mice, The Independent reported. This is an important step toward making artificial human blood, as doctors believe that figuring out a way to turn stem cells into blood artificially will eventually lead to this breakthrough. "This work is the culmination of over 20 years of striving," explained study researcher George Daley, The Independent reported.
With the political world deeply focused on the question of whether the Trump Administration comprises a gang of Russian pawns, less attention has been devoted to more mundane questions such as: what ever happened to Trump's economic policy? As it happens, economists are keeping their eye on that ball, and their conclusion is that it's in a bad way. Most specifically, they recognize that Trump policy is aimed heavily at achieving annual economic growth of more than 3%. During the Presidential campaign, Trump promised growth of 3.5% a year, and sometimes even 4%. There's no disagreement that a sustained growth rate of this magnitude would be a significant achievement.