"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
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.
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?
Currently, Artificial Intelligence (AI) is progressing at a great pace and deep learning is one of the main reasons for this, so all the people need to get a basic understanding of it. Deep Learning is a subset of Machine Learning, which in turn is a subset of Artificial Intelligence. Deep Learning uses a class of algorithms called artificial neural networks which are inspired by the way the biological neural network functions inside the brain. The advancement in the field of deep learning is due to the tremendous increase in computational power and the presence of a huge amount of data. Deep learning is very much efficient in problem-solving as compared to other traditional machine learning algorithms.