artificial intelligence | The Knowledge Dynasty

artificial intelligence

Rasa Core kicks up the context for chatbots

Context is everything when dealing with dialog systems. We humans take for granted how complex even our simplest conversations are. That’s part of the reason why dialog systems can’t live up to their human counterparts. But with an interactive learning approach and some open source love, Berlin-based Rasa is hoping to help enterprises solve their conversational AI problems.

The premise of Rasa Core is similar to the approach of a lot of AI startups that use services like Amazon Mechanical Turk to correct for uncertainty faced by machine learning models. But instead of Turk, Rasa built its own platform that allows anyone to train and update models by engaging in sample conversations with bots under construction.

You can see this playing out in the image above. Rasa Core suggests the most probable pre-programmed action that a given user is looking to perform. The trainer can then either reaffirm the correct decision or correct for an error. After a correction, the model adapts and the next time it’s faced with a similar situation, it won’t need to question it.

The Rasa team says that only a few dozen sample conversations are needed to get a bot working effectively. Of course extra samples can only serve to help increase accuracy and ultimately user friendliness for customers.

“We’ve seen conversations IBM built on their Watson tech and it was a little disappointing,” Florian Nägele, a PM for conversational AI and customer of Rasa at large European insurer Helvetica, told me in an interview. “You have one decision tree and you can’t take over context from one tree to another.”

The beauty of Rasa’s approach is that it allows customers to bootstrap models without training data. In a perfect world everyone has large corpuses of sample conversations that they can use to train dialog systems but this isn’t always the case — particularly for less technical enterprises.

Rasa Core is available now in open source via GitHub. The company also announced paid enterprise tiers for both Rasa Core and Rasa NLU. We covered Rasa NLU when it launched back in December 2016. The paid subscriptions will offer enterprises an administrative interface, customer support, automated testing and collaborative model training.

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Amazon to open visually focused AI research hub in Germany

Ecommerce giant Amazon has announced a new research center in Germany focused on developing AI to improve the customer experience — especially in visual systems.

Amazon said research conducted at the hub will also aim to benefit users of Amazon Web Services and its voice driven AI assistant tech, Alexa. 

The center will be based in Tübingen, near the Max Planck Institute for Intelligent Systems‘ campus, and will be staffed with more than 100 machine learning engineers.

The new 100+ “highly qualified” jobs will be created over the next five years, it said today. The site is Amazon’s fourth Research Center in Germany — after Berlin, Dresden and Aachen

For the Tübingen hub, the company is collaborating with the Max Planck Society on an earlier regional research collaboration that kicked off in December 2016 and is also focused on AI, as well as on bolstering a local startup ecosystem.

Robotics, machine learning and machine vision are key areas of focus for the so-called ‘Cyber Valley’ initiative. Existing partner companies in that effort include BMW, Bosch, Daimler, IAV, Porsche and ZF Friedrichshafen — and now Amazon.

As with other research partners, Amazon will be contributing €1.25 million to set up research groups in the Stuttgart and Tübingen regions, the Society said today.

“We appreciate Amazon’s commitment in the Cyber Valley and to research on artificial intelligence,” said Max Planck president Martin Stratmann in a statement. “We gain another strong cooperation partner who will further increase the international significance of research in the area of machine learning and computer vision in the Stuttgart and Tübingen region.”

“With our Amazon Research center in Tübingen, we will become part of one of the largest research initiatives in Europe in the area of artificial intelligence. This underlines our commitment to create high-skilled jobs in breakthrough technologies,” added Ralf Herbrich, director of machine learning at Amazon and MD of the Amazon Development Center Germany, in another supporting statement.

Earlier this month TechCrunch broke the news that Amazon had acquired 3D body model startup, Body Labs, whose scientific advisor and co-founder — Dr Michael J Black — is a director at the Max Planck Institute for Intelligent Systems’ Department of Perceptive Systems.

The Institute generally describes its goal being “to understand the principles of perception, learning and action in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems”.

Amazon said today that Dr Black will support its the new research hub as an Amazon Scholar, along with another Max Planck director, Dr Bernhard Schölkopf, who is based in the Department of Empirical Inference.

Both will also continue to manage their respective departments at the Institute, it added.

Schölkopf is a leading expert in machine learning in Europe and co-inventor of computer-aided photography. He has also developed pioneering technologies through which computer causality can be learned. With causality, AI systems predict customer behavior in response to automated decisions, such as the order of the search results, to optimize the search experience,” said Amazon. “Black is a leading expert in the field of machine vision and co-founder of the Body Labs company, which markets AI body procedures for capturing human body movements and shapes from 3D images for use in various industries.”

As we suggested at the time, Amazon’s purchase of the 3D body model startup looks primarily like a talent-based acquihire — to bring Black’s visual systems’ expertise into the fold.

Although the Max Planck Institute also manages and licenses thousands of patents — so smoother access, via Black’s connections, to key technologies for licensing purposes may also be part of its thinking as it spends a few euros to forge closer ties with the German research network.

Investing in business critical research and the next generation of AI researchers is also clearly on the slate here for Amazon: As part of the collaboration it says it will be providing the Society with research awards worth €420,000 per year.

A spokesperson confirmed this funding will be provided for five years, although it’s not clear exactly how many PhD candidates and Post-Doc research students will get funded from out of Amazon’s pot of money each year.

The Society said it will use the funding to finance the research activities of doctoral and postdoctoral students at the Max Planck Institute for Intelligent Systems.

“The support from Amazon and the other Cyber Valley partners enables us to further improve the training of highly qualified junior researchers in the field of artificial intelligence,” said Schölkopf in a statement. “This will help to ensure that we continue to provide both science and industry with creative minds to consolidate our pioneering position in intelligent systems.”

Computer vision has become a hugely important AI research area over the past decade — yielding powerful visual systems that can, for example, quickly and accurately detect and recognize objects, individual faces and body postures, which in turn can be used to feed and enhance the utility and intelligence of AI assistant systems.

And while CV research has already been fairly widely commercially applied by tech giants, there’s plenty of challenges remaining and academics continue to work on enhancing and expanding the power of visual AI systems — with tech giants like Amazon in close pursuit of any gains.

The basic rule of thumb is: The bigger the platforms, the bigger the potential rewards if smarter visual systems can shave operating costs and user friction from products and services at scale.

The Tübingen R&D hub is Amazon’s first German center focused on visual AI research. Though it’s just the latest extension of already extensive Amazon R&D efforts on this front (a quick LinkedIn job search currently lists ~470 Amazon jobs involving computer vision in various locations worldwide).

Amazon’s Berlin research hub started as a customer service center but since 2013 has also included dev work for the cloud business of Amazon Web Services (including hypervisors, operating systems, management tools and self-learning technologies).

While its Dresden hub houses the kernel and OS team that works on the core of EC2, the actual virtual compute instance definitions and Amazon Linux, the operating system for its cloud.

In Aachen its R&D hub houses engineers working on Alexa and architecting cloud AWS services.

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AI hype has peaked so what’s next?

2017 has been the year of AI, reaching a fever pitch of VC and corporate investment. But, as with any hot technology, AI is outgrowing this phase of experimentation and hype. According to research firm Gartner, we’re past the “peak of inflated expectations.

Next up is a necessary recalibration of the space—one that will separate the winning AI-driven companies from all the noise.

This next phase of AI is all about substance over flash; the focus now is on the hard work of building and strengthening real businesses that can go the distance.  Since 2012, the common exit for an AI company has been acquihire—acquired for talent or technology, not business performance—with many companies selling for below 50M.

Simultaneously, the behemoths of tech like Facebook, Google, and Amazon are innovating in AI at a rapid pace—displacing smaller competition by releasing new products or open sourcing more AI tools. In this highly competitive and rapidly evolving environment, cool tech isn’t enough. It’s essential for startups to build AI strongholds that can outlast the competition and compete with the big guys.

But, what are the prerequisites and ingredients for a winning AI-driven business?  As VCs evaluating the space, we’ve come up with a few key attributes that help us identify the strongest players. I like to call these attributes “moats.” Each additional “moat” helps secure your castle—widening the gap between you and your competition while creating more value for your customers.

  1. Proprietary Data

As an emerging competency in the enterprise, integrating AI functionality into products is giving companies a competitive edge—at least, for now. But, AI-as-a-Service is not far off. Google, Microsoft, Amazon, and Salesforce have made outsized investments in the space. Eventually we’ll see a democratization of AI capabilities as algorithms become increasingly more streamlined, standardized, and universally available. This leaves the true value in the data itself. Massaging a volume of data for use and getting it right is no easy feat. But startups need to ask themselves what the long-term value of their data is. Is it exclusively yours or can it be easily replicated or acquired by a competitor? The ability to acquire proprietary data is a key signal to us whether or not a company will eventually find itself part of a commoditized pack or can maintain meaningful differentiation long-term.

  1. Team Domain Expertise

A differentiated AI solution isn’t just about having a novel algorithm and throwing unique data at it; it’s about having a team that understands what to look for in the data—someone who can turn the knobs and dials and adjust the algorithm to help an AI system learn to recognize correct and incorrect answers. Contrary to popular wisdom, this person is not necessarily an engineer or a data scientist. It’s someone who can understand actuarial tables if you’re in the insurance business. Or, if you’re in defense, it’s someone who can understand the signals that are most discriminating in detecting potential threats. These domain experts give AI teams a leg up in making their products relevant, practical, and indispensable to their target markets. This level of specialized “human tuning” sets leading AI solutions apart from simply “renting” algorithms for general data processing.

  1. Workflow Position

So you have unique data and you built a team with both domain and technical acumen, but have you built a system that fits within the target users’ daily or regular work flow? The best AI solutions are those that exist in what I call an “operational loop,” where they are constantly being fed new data and seeing regular user engagement. A great example of this is, which analyzes daily sales recordings and provides performance recommendations. Not only does this type of position in the workflow increase customer stickiness—it also helps the system get smarter over time. Having a team with comprehensive domain knowledge comes into play here: is your AI system appropriately trained to ask the right questions for optimal pattern recognition and creation? Is it learning, adapting, and becoming an increasingly more essential piece of a user’s workflow? Human tuning is necessary at the outset of any AI learning process, but the system itself must develop and increase its performance for the specific customer. This ability drives inherent advantage over the static software available in the market.

  1. Degrees of Customer Value

What is the actual impact of your AI solution on a use case? What exactly are you doing better? Is it 10 times better or is it 100 times better than the status quo? For example: say you’re a doctor, and there’s a new piece of software that helps you analyze X-ray images a little bit more conveniently. It already filters out unnecessary visual info and zooms into the key areas where you’ll need to look.

That’s a tool, I’d say. It might help one doctor be more productive—a 2 to 5x multiplier. But if you take the same doctor and gave him a tool that went through all the images he needed to review in an afternoon and narrowed scope to only the priority images—let’s say, three out 500—that would be a “force multiplier”—a 10x value. The 100x is the most exciting, and that’s where a system could look through all the X-rays automatically, and go straight to a diagnosis. This level of near total automation requires a ton of trust, hence the value of domain experts to set it up and AI learning to capture data, patterns, and insights at scale.  As such, we try to invest in companies that land in the 10x value with the promise of eventually closing in on 100x.

If you’re one of the AI entrepreneurs out there: what are your AI moats? How defensible are they? What are you building that is stronger than just technology and talent? Enterprise businesses want and need you (and I want to hear your pitch). But only the strongest AI companies will be able to outlast the pack of startups and outpace the big tech companies. Over next five years, AI will continue to expand as a layer across every enterprise business process—from sales to marketing to customer service to product development to finance to operations.  How are you going to be a key player driving that transformation?

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