Taking place online on July 14, this year’s AI World Executive Summit will bring together innovators and thought leaders from across this diverse ecosystem and deliver key knowledge for participants to bring back to their organizations. deepsense.ai has been a strategic partner of AI World summits since the first conference and will again this year be supporting the event.

deepsense.ai VP of Engineering and Board Member Michał Iwanowski will share insights about the development of AI for enterprise during the session “To Make or to Buy – How to Get the Best of Both Worlds in Your AI Transformation.”

“I’m always pleased to actively participate in AI World conferences, because it is a great place to discuss AI applications with practitioners. This year I would like to present deepsense.ai’s expertise in the area of successful enterprise AI deployments. There is no question that enterprises that can successfully leverage the potential of AI will be the ones to get ahead. While businesses are certain about the goals they want to use AI for, how to execute and deploy it successfully remains a difficult question. I’m looking forward to providing some useful insights on that question,”
– Michał says.

The AI World Executive Summit is organized by AI Trends, the leading industry media channel focused on the business and technology of AI.

deepsense.ai has been selected to be a part of Silicon Valley Innovation Platform run by the Plug and Play Tech Center, the world’s largest early stage investor, accelerator, and corporate innovation hub. During the selection process deepsense.ai beat out over 1000 innovative tech companies from all over the world.

The mission of the Plug and Play Tech Center is to drive innovation by connecting entrepreneurs, corporations, and investors worldwide. With its expanded ecosystem consisting of over 500 corporate partners and 200 venture capitalists, Plug and Play matches the world’s largest corporations with cutting-edge technology. Plug and Play was an early investor in Google, PayPal, Dropbox, LendingClub, N26, Soundhound, Honey, Kustomer, and Guardant Health.

‘To be recognised by such a prestigious organization as Plug and Play is a tremendous achievement for our company. I’m extremely proud of deepsense.ai’s development and excited about the new business opportunities the Silicon Valley Innovation Platform can bring us,’ says Michał Iwanowski, VP of Engineering at deepsense.ai.

As part of the acceleration process, deepsense.ai will participate in a 12-week Insurtech business development program focused on tech innovations in the insurance industry.

“Model-Based Reinforcement Learning for Atari,” a research paper written by a consortium of authors, has been chosen for a spotlight presentation at the International Conference on Learning Representation. Only 5% of the papers submitted receive this distinction. Two of the leading authors, Piotr Miłoś and Błażej Osiński, are both researchers and senior data scientists at deepsense.ai.

The paper describes research conducted in cooperation with Google Brain, the University of Warsaw and the University of Illinois at Urbana-Champaign.

The high-level goal described in the research is how to enable an AI model to learn useful behaviors by interacting with its environment. In this case, the environment was Atari games, which offer tasks that are simple yet challenging enough to test the research concept. The results of the experiment can be extrapolated to more challenging tasks in increasingly complex environments. The model the deepsense.ai researchers designed learned how to play the games thanks to an extensive chain of trial and error.

An extension of the research might be a robot acquiring skills while moving objects, such as putting dishes in a dishwasher or refrigerator. Such interactions can be costly – the robot will drop any number of dishes along the way, so it takes time and resources for it to train itself thoroughly enough to learn to carry out its tasks efficiently.

To tackle the challenge of expensive interactions, we increase the system’s learning efficiency by endowing it with a predictive module that can imagine the consequences of actions without actually executing them. The module is trained along with this skill and it is used to perform short inner simulations. These simulations substitute most of the interactions normally required by reinforcement learning, resulting in better sample efficiency.

Details about the research are available in the full arXiv preprint, while the press release and an article about artificial imagination can be found on deepsense.ai’s blog.

“We are proud and excited to see our work recognized at one of the most renowned scientific conferences in the world,”
– says Piotr Miłoś, senior data scientist at deepsense.ai and a professor at the Polish Academy of Science.

Reinforcement learning in spotlight

“Spotlight” is a short oral presentation reserved for papers of particular distinction. With only 5% of submitted papers honored this way, the privilege comes amidst our work gaining critical attention in the AI research community. The arXiv paper has been cited more than 50 times in just 10 months, a testament to its significance for other researchers in the field.

This year’s conference, the eighth, will be held in Addis Ababa, Ethiopia, and is hoped to encourage researchers from Africa and the Middle East to take part. Despite its short history, the conference is currently among the most important deep learning-related scientific events  in the world. The conference publication is recognized on Google scholar as the 42nd most important scientific publication globally.

The conference provides an interesting insight into the development of Artificial Intelligence and is a great way to keep abreast of the latest trends. deepsense.ai has recently used machine learning tools to review the papers submitted to the conference. The analysis showed that reinforcement learning is among the most significant and recurrent trends in machine learning today. Further information on trending topics on the ICLR conferences can be found in deepsense.ai’s analysis.

“Presenting our research on spotlight is a great honor and proof that our contribution to the machine learning community and development is recognized,”
– concludes Błażej Osiński, a senior data scientist and deepsense.ai researcher involved in the project.

This success is the result of deepsense.ai’s strong commitment to R&D. Just a month ago two other papers were presented at NeurIPS workshops, another top-tier AI conference. These included “Developmentally motivated emergence of compositional communication via template transfer” and “Simulation-based reinforcement learning for real-world autonomous driving”.

The full list of paper authors: Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski

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About deepsense.ai:

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deepsense.ai and Volkswagen show that an autonomous vehicle trained entirely in simulation can drive in the real world. The team transferred its neural network into a real car, providing it with over 100 years’ driving experience before the real-world engine had ever started.

Researchers from deepsense.ai recently presented their work on training an autonomous vehicle in a simulated environment and then transferring the model into a real-world vehicle. The team presented their paper during a workshop at the 2019 Neural Information Processing System conference in Vancouver, BC.

“Moving the neural network policy from the simulator to reality was a breakthrough and the heart of the experiment. We have run multiple training sessions in the simulated environment, but even the most sophisticated simulator delivers different experiences than what a car encounters in the real world. This is known as the sim-to-real gap. Our algorithm has learned to bridge this gap,” explains Piotr Miłoś, deepsense.ai researcher and a professor at the Polish Academy of Sciences. „That we actually rode in a car controlled by a neural network proves that reinforcement learning-powered training is a promising direction for autonomous vehicle research overall.”

The experiment was conducted by a team of researchers including deepsense.ai’s Błażej Osiński, Adam Jakubowski, Piotr Miłoś, Paweł Zięcina and Christopher Galias, Volkswagen researcher Silviu Homoceanu and University of Warsaw professor Henryk Michalewski. The team not only trained a model that controls a car in a simulated environment but also executed test drives in a real car at Volkswagen’s testing facility. The car navigated through real streets and crossroads, performing all sorts of real-world driving maneuvers.

Over 100 years of driving experience

The technique the deepsense.ai-Volkswagen team used was innovative for two reasons. First, the neural network was trained in a simulated environment and later transferred to a real car. Second, the neural network was trained using a reinforcement learning paradigm.

Being able to use simulation offers crucial advantages. First, it is cheaper. In their combined experiments, the team generated some 100 years of simulated driving experience. Racking up so much experience in a real car isn’t feasible. Training in a simulator can also be done much faster. The agent can experience all manner of danger, from simple rain to deadly extreme weather, accidents and full-blown crashes, and learn to navigate or avoid them. Subjecting an actual driver to such hazards would be prohibitively complicated, time-consuming and ethically unacceptable. What’s more, extreme scenarios in the real world are relatively uncommon but can be brought on in a simulator at the snap of the fingers.

“It was exciting to see that our novel approach worked so well. By further exploring this path we can deliver more reliable and flexible models to control autonomous vehicles,” explains Błażej Osiński. “We tested our technique on cars, but it can be further explored for other applications.”

Shaping the behavior

Reinforcement learning allows a model to shape its own behavior by interacting with the environment and receiving rewards and penalties as it goes. The model’s goal is, ultimately, to maximize the rewards it receives while avoiding penalties. An autonomous car receives points for driving safely and complying with the traffic laws.

Reinforcement learning delivers a more flexible model that can adjust itself to a changing environment and react accordingly in new situations. Simulating every road condition that can occur is impossible, but shaping a set of guidelines like “avoid hitting objects” or “protect passenger from any and all harm” is not. Ensuring the model sticks to the guidelines makes it more reliable, including in less common situations it will eventually face on the road.

Technical details of the research are described in the team’s white paper, available on arxiv. At the 2019 Neural Information Processing Systems conference in Vancouver, BC,  over ten thousand participants took part in sessions and workshops on artificial intelligence. The team presented its paper during the workshop Machine Learning for Autonomous Driving (https://ml4ad.github.io/) organized by researchers from UC Berkeley, Carnegie Mellon University, Columbia University, and Waymo.

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About deepsense.ai:

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Leading machine learning and artificial intelligence-based solutions provider deepsense.ai has named  Andy Thurai as head of its US Operations. Mr. Thurai gained broad experience at tech companies including IBM, Oracle, BMC and Intel, where he was responsible for product strategy, thought leadership and business development.

“I am happy and proud to invite Andy on board,” said Tomasz Kułakowski, deepsense.ai’s Chief Executive Officer. “Andy is a seasoned strategist with a strong tech background and thorough understanding of the cutting-edge technology we are dealing with at deepsense.ai. His involvement will lift deepsense.ai into a new era and further support US operations.”

Mr. Thurai worked as a chief strategist for IBM and an Emerging Technology Strategist for Oracle Cloud Infrastructure. He was a field CTO with Intel and BMC. He also has extensive experience as a consultant, technology advisor and mentor.

“deepsense.ai is a technological trail-blazer and one of the most innovative companies in the world, delivering outstanding results and operating on the cutting-edge tech,” Mr Thurai said. “I am truly excited to join the company and help change the world for the better with my experience. It is going to be a great adventure!”

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About deepsense.ai:

brand-manager-at-deepsense