To judge from the hype, artificial intelligence is inches away from ripping through the economy and destroying everyone's jobs--save for the AI scientists who build the technology and the baristas and yoga instructors who minister to them. But one critic of that view comes from within the tent of AI itself: Gary Marcus. From an academic background in psychology and neuroscience--rather than computer science--Mr Marcus has long been an AI gadfly. He relishes poking holes in the popular AI technique of deep-learning because of its inability to perform abstractions even as it does an impressive job at pattern-matching. Yet his unease with the state of the art didn't prevent him from advancing the art with his own AI startup, Geometric Intelligence, which he sold to Uber in 2016.

Until recently, a multinational company looking to help customers around the world book international travel would have had to build separate chatbots from scratch to converse in French, Hindi, Japanese or other languages. But thanks to artificial intelligence research breakthroughs that have enabled algorithms to more accurately parse nuances in the way different languages express concepts or structure sentences, it is now possible to build a single bot and use Microsoft Translator to translate questions and answers accurately enough for use in multiple countries. Over the past few years, Microsoft deep learning researchers were the first to achieve human parity milestones in developing algorithms that could perform about as well as a person on research benchmarks testing conversational speech recognition, reading comprehension, translation of news articles and other challenging language understanding tasks. Now, the benefits of those AI research breakthroughs are making their way into products from Azure to Bing. Search engineers are borrowing lessons from Microsoft AI researchers who developed a new deep neural network model that can learn from multiple natural language understanding tasks at once.

Measuring the intelligence of AI is one of the trickiest but most important questions in the field of computer science. If you can't understand whether the machine you've built is cleverer today than it was yesterday, how do you know you're making progress? At first glance, this might seem like a non-issue. "Obviously AI is getting smarter" is one reply. "Just look at all the money and talent pouring into the field. Look at the milestones, like beating humans at Go, and the applications that were impossible to solve a decade ago that are commonplace today, like image recognition. How is that not progress?"

"Q: How worried do you think we humans should be that machines will take our jobs? A: It depends what role machine intelligence will play. Machine intelligence in some cases will be useful for solving problems, such as translation. But in other cases, such as in finance or medicine, it will replace people." This Q&A is taken from Tom Standage's description of how he interviewed AI (language model GPT-2) for The Economist The World in 2020. As readers of this column's annual roundup of AI predictions know, this year's first installment of 120 AI predictions for 2020 featured my interview of Amazon AI in which Alexa performed slightly better than the previous year. For the new list of 99 additional predictions, I repeated Standage's question to Alexa, and got the response "Hmm, I'm not sure." The following AI movers and shakers are a lot more confident in what the near future of machine intelligence will look like, from robotic process automation (RPA) to human intelligence augmentation (HIA) to natural language processing (NLP).

But tagging everything with the right metadata quickly presents a labor problem: even though manual tagging is an important part of the process, the DTCI Technology team doesn't have time to manually categorize every frame. That's why Farré's team has put machine learning--and more recently, deep learning--to the task of generating metadata. The goal is to build deep-learning algorithms that can automatically tag the components of a scene in a way that's consistent with the rest of the Disney knowledge base. Humans still need to approve the algorithm's tags, but the project is meaningfully reducing the work that goes into organizing the Disney library, improving the accuracy of searches within it. What's more, this progress is freeing up engineers to focus more on developing deep-learning models using AWS (Amazon Web Services).

With 51 workshops, 1428 accepted papers, and 13k attendees, saying that NeurIPS is overwhelming is an understatement. I did my best to summarize the key trends I got from the conference. This post is generously edited by the wonderful Andrey Kurenkov. Disclaimer: This post doesn't reflect the view of any of the organizations I'm associated with. NeurIPS is huge with a lot to take in, so I might get something wrong.

So, you want to build your career in the next big thing, actually, the biggest phenomenon to sweep the world since the Internet. Welcome, but you're not alone. Millions of aspirants across the planet are gearing up their skills and knowledge, getting accredited and certified in the sexier-than-sexiest career of the 21st century (sorry, Harvard Business Review). So what will set you apart? An artificial intelligence engineer conceptualizes, designs, builds and finally rolls out sophisticated machine learning algorithms that facilitate autonomous knowledge gathering through unstructured training sets (deep learning) besides the deployment of the AI models in real-world production setups.

The artificial intelligence sector sees over 14,000 papers published each year. This field attracts one of the most productive research groups globally. AI conferences like NeurIPS, ICML, ICLR, ACL and MLDS, among others, attract scores of interesting papers every year. The year 2019 saw an increase in the number of submissions. This year also saw noticeable trends like the increased usage of PyTorch as a framework for research increased by 194% among many others.

Hyderabad: As scientific disciplines go, the field of'Deep Learning' is but an infant. However, it will soon have a disruptive effect on the field of drug design, experts say. They were speaking at a panel discussion as part of the ongoing international conference on high performance computing in Hyderabad on Wednesday. Deep learning, a subset of machine learning, functions by imitating the workings of the human brain to process large amounts of data. The artificial neural networks used in these functions have neuron nodes connected together like a web.

In August, Google AI researchers working with the ALS Therapy Development Institute shared details about Project Euphonia, a speech-to-text transcription service for people with speaking impairments. They showed that, using data sets of audio from both native and non-native English speakers with neurodegenerative diseases and techniques from Parrotron, an AI tool for people with impediments, they could drastically improve the quality of speech synthesis and generation. Recently, in something of a case study, Google researchers and a team from Alphabet's DeepMind employed Euphonia in an effort to recreate the original voice of Tim Shaw, a former NFL football linebacker who played for the Carolina Panthers, Jacksonville Jaguars, Chicago Bears, and Tennessee Titans before retiring in 2013. Roughly six years ago, Shaw was diagnosed with ALS, which requires him to use a wheelchair and left him unable to speak, swallow, or breathe without assistance. Over the course of six months, the joint research team adapted a generative AI model -- WaveNet -- to the task of synthesizing speech from samples of Shaw's voice prior to his ALS diagnoses.