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) …
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Physically taxing jobs can hinder one's cognitive health, potentially causing a person's brain to age faster and leave them with a poorer memory as they grow older, a new study suggests. In a study published in the Frontiers in Human Neuroscience in July, researchers surveyed nearly 100 cognitively healthy older adults between ages 60 and 80 years old in order to better understand how stress plays a role in how the human brain ages. Their analysis indicated that adults who reported having higher levels of physical stress in their most recent job were also people who had a smaller hippocampal volume and poorer memory performance. The hippocampus is commonly associated with memory.
Syntiant Corp., the "neural decision processor" startup, announced completion of another funding round this week along with the shipment of more than 1 million low-power edge AI chips. The three-year-old startup based in Irvine, Calif., said Tuesday (Aug. The round was led by Microsoft's (NASDAQ: MSFT) venture arm M12 and Applied Ventures, the investment fund of Applied Materials (NASDAQ: AMAT). New investors included Atlantic Bridge Capital, Alpha Edison and Miramar Digital Ventures. Intel Capital was an early backer of Syntiant, part of a package of investments the chip maker announced in 2018 targeting AI processors that promise to accelerate the transition of machine learning from the cloud to edge devices.
Rigetti Computing, a leading quantum computing startup and pioneer in hybrid quantum-classical computing systems, has announced it closed a $79M Series C financing led by Bessemer Venture Partners. Franklin Templeton joins the round with participation from Alumni Ventures Group, DCVC, EDBI, Morpheus Ventures, and Northgate Capital. "This round of financing brings us one step closer to delivering quantum advantage to the market," said Chad Rigetti, founder and CEO of Rigetti Computing. The company is dually focused on building scalable, error-corrected quantum computers and supporting high-performance access to current systems over the cloud. Rigetti offers a distinctive hybrid computing access model designed for practical applications.
OpenAI's GPT-3 is the talk of the town, and the media is giving it all the attention. Many analysts are even comparing it to AGI because of its practical applicability. Initially disclosed in a research paper in May, GPT-3 is the next version of GPT-2 and is 100x larger than it. It is far more competent than its forerunner due to the number of parameters it is trained on, which is 175 billion for GPT-3 versus 1.5 billion for GPT-2. After the successful launch of GPT-3, other AI companies seem to have been overshadowed.
Brain-computer interfaces are seeing massive AI breakthroughs including neural bridges being built for learning, treatment of specific diseases and overcoming the electrical-to-biochemical language barrier. These trends are what will optimise the information bandwidth that comes with neuroscience technology. "A monkey has been able to control a computer with its brain." That almost unimaginable yet remarkably accurate observation was made by Elon Musk, author and CEO of Tesla. In his presentation, Musk switched between varying forms of "what is" to "what could be", before announcing the details surrounding Tesla Energy.
Today's hybrid IT environments, which incorporate cloud and on-premise infrastructure, demand structural changes to agency security operations centers, or SOCs, to be better able to operate within cyberspace versus simply reacting to it. The structure of SOCs is already adapting and evolving to bring together defensive operations and the analysis of emerging threats with the strategic introduction of new technologies. The result is a mature, flexible, risk-based and cost-efficient approach to ensure the crown jewels of an enterprise remain secure. One key to succeeding in this environment is to apply both automation and orchestration. Automation is applied to both defense operations and threat hunting, using a combination of artificial intelligence and machine learning.
If you think neural nets are black boxes, you're certainly not alone. While they may not be as interpretable as something like a random forest (at least not yet), we can still understand how they process data to arrive at their predictions. In this post we'll do just that as we build our own network from scratch, starting with logistic regression. If you think neural nets are black boxes, you're certainly not alone. While they may not be as interpretable as something like a random forest (at least not yet), we can still understand how they process data to arrive at their predictions.
The 2.2M parameters in MobileNet are frozen, but there are 1.3K trainable parameters in the dense layers. You need to apply the sigmoid activation function in the final neurons to ouput a probability score for each genre apart. By doing so, you are relying on multiple logistic regressions to train simultaneously inside the same model. Every final neuron will act as a seperate binary classifier for one single class, even though the features extracted are common to all final neurons. When generating predictions with this model, you should expect an independant probability score for each genre and that all probability scores do not necessarily sum up to 1. This is different from using a softmax layer in multi-class classification where the sum of probability scores in the output is equal to 1.
For movie buffs, the work that the factory machines do in Charlie Chaplin's 1936 classic, Modern Times, may have seemed too futuristic for its time. Fast forward eight decades, and the colossal changes that Artificial Intelligence is catalyzing around us will most likely give the same impression to our future generations. There is one crucial difference though: while those advancements were in movies, what we are seeing today are real. A question that seems to be on everyone's mind is, What is Artificial Intelligence? The pace at which AI is moving, as well as the breadth and scope of the areas it encompasses, ensure that it is going to change our lives beyond the normal.
Depending on your opinion, Artificial Intelligence is either a threat or the next big thing. Even though its deep learning capabilities are being applied to help solve large problems, like the treatment and prevention of human and genetic disorders, or small problems, like what movie to stream tonight, AI in many of its forms (such as machine learning, deep learning and cognitive computing) is still in its infancy in terms of being adopted to generate software code. AI is evolving from the stuff of science fiction, research, and limited industry implementations, to adoption across a multitude of fields, including retail, banking, telecoms, insurance, healthcare, and government. However, for the one field ripe for AI adoption – the software industry – progress is curiously slow. Consider this: why isn't an industry, which is built on esoteric symbols, machine syntax, and repetitive loops and functions, all-in on automating code?