This playlist contains the lecture videos for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence. The ambiguities and noise inherent in human communication render traditional symbolic AI techniques ineffective for representing and analysing language data. Recently statistical techniques based on neural networks have achieved a number of remarkable successes in natural language processing leading to a great deal of commercial and academic interest in the field This is an applied course focusing on recent advances in analysing and generating speech and text using recurrent neural networks.

Global leader in digitally-powered business process management and services Genpact has launched its Artificial Intelligence (AI) Reporting solution that harnesses the power of AI technologies to automate financial planning and analysis (FP&A) operations and drive more timely, insightful reporting. CFOs have fast, seamless access to both internal and external data sources like never before, driving more accurate forecasts for quicker, smarter business decisions. Based on multiple studies including a study done jointly by the Genpact Research Institute and HfS Research, CFOs across the board struggle with three challenges in providing actionable insights to their business leaders, including the ability to get relevant disparate data from internal and external sources; delay and the effort it takes for their teams to manage and do basic analysis on that data, reducing its relevance and leaving little scope for predictive analytics and business insight value adds; and lack of reporting mechanism that creates the user experience for business leaders to use that data to drive real time actions. Genpact's cloud-based AI Reporting solution uses advanced digital technologies to help enterprises reimagine the end-to-end financial reporting process. By integrating structured and unstructured data from internal and external sources, and automating reporting processes with predictive analytics, natural language processing and generation, and machine learning, the solution drives greater agility to adapt to new business requirements.

There are endless discussions on the databases arena about which DBMS is best suited for operational or data warehousing analytics, which one is the most efficient for online transaction processing, or which one is suitable for semantic integration. Recently graph databases are growing in popularity, especially in the enterprise space, and perhaps that adds more headache on those vendors that try to differentiate from competition and on those clients that are completely uncertain how to embrace this database technology. Recently Bloor published a report about Graph and RDF Databases. The author, Philip Howard, claims that "the difference between a true graph product and a triple store is that the former supports index free adjacency (which means you can traverse a graph without needing an index) and the latter doesn't". On the contrary Weinberger, CEO of ArrangoDB, argues that this is not a fundamental criterion on what is a graph database.

On a chilly March afternoon last year in the South Korean capital Seoul, a computer algorithm made history. A program called AlphaGo beat the reigning human world champion at go, an ancient Chinese board game considered to be one of the most complex pastimes man has ever devised. The game has remained an inviolably human pursuit for centuries, and one of the hardest challenges for artificial intelligence (AI) because of the vast number of possible moves -- more than the number of atoms in the universe -- and the need to employ creativity to win. In Seoul's Four Seasons hotel, AlphaGo's victory over five games was ruthless: Lee Sedol, the 33-year-old human go grandmaster, lost 4-1. At a press conference afterwards, he said with a trace of wonder: "Today, I am speechless."

The healthcare technology sector has given rise to some of the most innovative startups in the world, which are poised to help people live longer, better lives. The innovations have primarily been driven by the advent of software and mobility, allowing the health sector to digitize many of the pen and paper-based operations and processes that currently slow down service delivery. More recently, we're seeing software become far more intelligent and independent. These new capabilities -- studied under the banner of artificial intelligence and machine learning -- are accelerating the pace of innovation in healthcare. Upon close evaluation of the opportunities that exist within each area, it becomes obvious that the stakes are high.

From the day we founded Spare5, we had a somewhat crazy goal. Okay, we actually had (and have) quite a few ambitious goals. The one that is top of mind for me today is creating a sense of community among a global, diverse group of people who choose to spend their spare time in our app and website. We affectionately refer to our users as "Fives." Everyone has passions, skills, insights, and free time.

There's long been talk in medicine about the need for doctors to listen to patients. Now that advice is being taken literally to diagnose concussions and other hard-to-diagnose brain disorders. Whether caused by a blow to the head during a football game or an accident, concussions are particularly hard to identify. A Utah-based startup is easing that process using AI and the patient's voice to detect telltale shifts in vocal patterns -- shifts human ears can't pick up -- to help doctors make the right call. The company, Canary Speech, is building voice tests that use GPU-accelerated deep learning to pick up the subtle voice tremors, slower speech and gaps between words that may reveal brain injuries, or warn of diseases such as Parkinson's or Alzheimer's.

Just under a year after their $12.5 million Series A funding round, Israeli Artificial Intelligence for ecommerce startup Twiggle announced today the release of their Semantic API product, bringing their accumulated expertise in search to the wider online shopping market. Twiggle was co-founded in December of 2013 by CEO Dr. Amir Konigsberg, previously one of the members of Google's emerging markets operations, and Dr. Adi Avidor, a former engineering tech lead at Google. In the time since their Series A, they have picked up another $5 million from Alibaba, doubled their team, and moved shop to new offices overlooking Tel Aviv. Describing what they have built in short, this company has made search for ecommerce usable to the point that it becomes a near enjoyable experience. Their engine processes through lists of products, understanding the attributes that make them what they are.

Build Cool AI Technology in 90 Minutes and Learn by Doing (No Coding!) If you're reading this, you probably know that, arguably, artificial intelligence is the most significant technological developments of our time. There will be people who are able to use AI to do incredible things and others who watch technology and the world change around them at a blistering pace without understanding fully what's going on. If you're someone who stays "in the know," you need to learn about AI. We think that you can best learn by doing.

Let's talk about the millennial generation -- the demographic segment so coveted that marketing managers from all over the globe are fighting over them. Are they really such a complex generation that we must resort to artificial intelligence to figure out what they want and to keep them coming back for more? Turns out they are, and AI is indeed the ultimate weapon in the fight for the millennial generation's ever-shortening attention span. Luckily, rapid strides in the field of machine learning will help unravel what this fickle "target market" really wants. Machine learning is a crystal ball in the world of AI.