The Department of Economic and Social Affairs at the United Nations projects that 9.7 billion people will inhabit the Earth come 2050. With one in eight people today not getting enough food, farmers will have to become more prolific in order to serve these additional billions. As nearly half of the planet's 10 global hectares of potentially productive land is already devoted to agriculture, any expansion will increasingly impact delicate ecosystems that are already declining. What's worse is the World Bank estimates that climate change could cut crop yields by more than 25 percent as the population continues to grow. Feeding this amount of people will require finding new ways of becoming even more efficient at producing food.

When I tell people that I work at an AI company, they often follow up with "So what kind of machine learning/deep learning do you do?" This isn't surprising, as most of the market attention (and hype) in and around AI has been centered around Machine Learning, and its high profile subset, Deep Learning, and around Natural Language Processing, with the rise of the chatbot and virtual assistants. But while machine learning is a core component for artificial intelligence, AI is in fact more than just ML. So what does it really mean for an application to be "intelligent"? What does it take to create a system that is "artificially intelligent?

So far, 2017 has been the year of AI/Machine Learning startups for marketing, search, content and social media. It's been fascinating to see all of the new companies popping up. Each week on the VentureBeat VB Engage podcast, it seems that Stewart Rogers and I are talking about yet another new startup that is using AI, deep learning, or bots to help it perform tasks that help make marketers more proficient in their campaigns, planning, or content. Recently at SXSW, Entrepreneur mogul, Mark Cuban, stated that the world's first trillionaire will be an artificial intelligence entrepreneur. Let's talk about a few startups in deep learning and artificial intelligence that have recently popped up on my radar at VentureBeat.

There is a tendency with any new technology to believe that it requires new management approaches, new organizational structures, and entirely new personnel. That impression is widespread with cognitive technologies -- which comprises a range of approaches in artificial intelligence (AI), machine learning, and deep learning. Some have argued for the creation of "chief cognitive officer" roles, and certainly many firms are rushing to hire experts with deep learning expertise. "New and different" is the ethos of the day. But we believe that successful firms can treat cognitive technologies as an opportunity to evolve or grow from previous work.

For something so simple, it turns out it's amazingly hard for compilers to speed up without a lot of human intervention. This is the heart of the GEMM matrix multiply function, which powers deep learning, and every fast implementation I know has come from old-school assembler jockeys hand-tweaking instructions! When I first started looking at the engineering side of neural networks, I assumed that I'd be following the path I'd taken on the rest of my career and getting most of my performance wins from improving the algorithms, writing clean code, and generally getting out of the way so the compiler could do its job of optimizing it. Instead I spend a large amount of my time worrying about instruction dependencies and all the other hardware details that we were supposed to be able to escape in the 21st century. Matrix multiplies are a hard case for modern compilers to handle.

Deep learning is an aspect of artificial intelligence (AI) that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge. At its simplest, deep learning can be thought of as a way to automate predictive analytics. Learn how to gain executive approval and drive operational, cultural changes within your organization. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.

As society enters an era where AI will take life or death decisions--spotting whether moles are cancerous and driving us to work--trusting these machines will become ever more important. The difficulty is that it's almost impossible for us to understand the inner workings of many modern AI systems that perform human-like tasks, such as recognizing real-life objects or understanding speech. The models produced by the deep-learning systems that have powered recent AI breakthroughs are largely opaque, functioning as black boxes that spit out a result but whose operation remains mysterious. This inscrutability stems from the complexity of the large neural networks that underpin deep-learning systems. These brain-inspired networks are interconnected layers of algorithms that feed data into each other and can be trained to carry out specific tasks.

If you're a software engineer (or someone who's learning the craft), chances are that you've heard about deep learning (which we'll sometimes abbreviate as "DL"). It's an interesting and rapidly developing field of research that's now being used in industry to address a wide range of problems, from image classification and handwriting recognition, to machine translation and, infamously, beating the world champion Go player in four games out of five. A lot of people think you need a PhD or tons of experience to get a job in deep learning, but if you're already a decent engineer, you can pick up the requisite skills and techniques pretty quickly. Important point: You need motivation and the ability to code and problem solve well. Here at Deepgram we're using deep learning to tackle the problem of speech search.

Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: -Data insufficiency:Often in healthcare predictive modeling, the sample size is insufficient for deep learning methods to achieve satisfactory results. -Interpretation:The representations learned by deep learning methods should align with medical knowledge. To address these challenges, we propose a GRaph-based Attention Model, GRAM that supplements electronic health records (EHR) with hierarchical information inherent to medical ontologies. Based on the data volume and the ontology structure, GRAM represents a medical concept as a combination of its ancestors in the ontology via an attention mechanism. We compared predictive performance (i.e. accuracy, data needs, interpretability) of GRAM to various methods including the recurrent neural network (RNN) in two sequential diagnoses prediction tasks and one heart failure prediction task. Compared to the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely observed in the training data and 3% improved area under the ROC curve for predicting heart failure using an order of magnitude less training data. Additionally, unlike other methods, the medical concept representations learned by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits intuitive attention behaviors by adaptively generalizing to higher level concepts when facing data insufficiency at the lower level concepts.

Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.