Artificial intelligence is the ability of machines to perform tasks usually associated with human beings. It includes concepts such as machine learning, deep learning, neural networks, natural language processing, and visual recognition. AI uses supervised learning, unsupervised learning, reinforcement learning, and deep learning to learn and train models with data.

Smart technology, artificial intelligence and machine learning are grabbing headlines every day, and those familiar buzzwords are now inescapable. Algorithms are being enhanced and scientists are coming up with new ways to train and teach these models. According to VentureBeat, machine learning is also shaping business and society. The publication spoke to five leading artificial intelligence experts for their input on what we'd see happen in machine learning in the new year. PyTorch creator Soumith Chintala, University of California professor Celeste Kidd, Google AI chief Jeff Dean, Nvidia director of machine learning research Anima Anandkumar, and IBM Research director Dario Gil said great strides were made in several fields in 2019, like natural language-based models and reinforcement learning, but the five AI experts were essentially unanimous in predicting an even more exciting 2020.

Powerful and disruptive: Learn the concepts behind a new BERT, getting rid of RNNs, CNNs and other heavy deep learning models to implement a more intuitive way to process language that will suit a wide range of NLP purposes, including yours! User-friendly and efficient: We've designed the course using the latest technologies, using Tensorflow 2.0 and Google Colab, assuring that you won't have any local machine/software version/compatibility issues and that you are using the most up-to-date tools. Powerful and disruptive: Learn the concepts behind a new BERT, getting rid of RNNs, CNNs and other heavy deep learning models to implement a more intuitive way to process language that will suit a wide range of NLP purposes, including yours! User-friendly and efficient: We've designed the course using the latest technologies, using Tensorflow 2.0 and Google Colab, assuring that you won't have any local machine/software version/compatibility issues and that you are using the most up-to-date tools.

As we enter our third decade in the 21st century, it seems appropriate to reflect on the ways technology developed and note the breakthroughs that were achieved in the last 10 years. The 2010s saw IBM's Watson win a game of Jeopardy, ushering in mainstream awareness of machine learning, along with DeepMind's AlphaGO becoming the world's Go champion. It was the decade that industrial tools like drones, 3D printers, genetic sequencing, and virtual reality (VR) all became consumer products. And it was a decade in which some alarming trends related to surveillance, targeted misinformation, and deepfakes came online. For better or worse, the past decade was a breathtaking era in human history in which the idea of exponential growth in information technologies powered by computation became a mainstream concept.

No other technology was more important over the past decade than artificial intelligence. Stanford's Andrew Ng called it the new electricity, and both Microsoft and Google changed their business strategies to become "AI-first" companies. In the next decade, all technology will be considered "AI technology." And we can thank deep learning for that. Deep learning is a friendly facet of machine learning that lets AI sort through data and information in a manner that emulates the human brain's neural network.

QuEST Global, a global product engineering and lifecycle services company, will demonstrate Deep Learning driven Advanced Driver Assistance Systems (ADAS) at CES (Consumer Electronic Show) 2020. The deep learning models developed by QuEST Global aim to enhance ADAS by improving the accuracy in detection of traffic signs, pedestrians and traffic. This enhanced ADAS will be demonstrated at Booth # 1909, Westgate Pavilion. The ADAS demo has been developed by training deep learning models using synthetic data representing various environmental conditions and terrains. Such deep learning driven ADAS are 25% more accurate than the ones developed using classic image processing techniques.

AI is no longer poised to change the world someday; it's changing the world now. As we begin a new year and decade, VentureBeat turned to some of the keenest minds in AI to revisit progress made in 2019 and look ahead to how machine learning will mature in 2020. We spoke with PyTorch creator Soumith Chintala, University of California professor Celeste Kidd, Google AI chief Jeff Dean, Nvidia director of machine learning research Anima Anandkumar, and IBM Research director Dario Gil. Everyone always has predictions for the coming year, but these are people shaping the future today -- individuals with authority in the AI community who treasure scientific pursuit and whose records have earned them credibility. While some predict advances in subfields like semi-supervised learning and the neural symbolic approach, virtually all the ML luminaries VentureBeat spoke with agree that great strides were made in Transformer-based natural language models in 2019 and expect continued controversy over tech like facial recognition. They also want to see the AI field grow to value more than accuracy.

It's time to welcome the new year with a splash of machine learning sprinkled into our brand new resolutions. Machine learning will continue to be at the heart of what we do and how we do it. What a year it has been! The sheer amount of developments we saw in Natural Language Processing (NLP) blew us away. It was the year of fine-tuning language models and frameworks like Google's BERT and OpenAI's GPT-2 (more of all of this later!).

Google Health and DeepMind have created an AI tool capable of spotting breast cancer with as much accuracy as a human radiologist, according to a new paper published in Nature on Wednesday. To train the model the researchers used two data sets of breast scans from the UK and the US. The UK dataset included scans from 25,856 women, while the US set contained mammograms from 3,097 women. Applying the AI resulted in a reduction of false negatives of 9.4% for the US dataset and 2.7% for the UK. There was a slightly smaller reduction for false positives, 5.7% for the US dataset and 1.2% for the UK one.