deepsense.ai, a leading AI-based end-to-end solutions provider with the focus on computer vision, predictive analytics and natural language processing, today announced that Nina Simosko has been appointed to the company’s advisory board. Ms. Simosko, formerly the CEO of NTT Innovation Institute, the prestigious Silicon Valley-based innovation center for NTT Group, one of the world’s largest ICT companies, is a well-known Silicon Valley technology executive with senior management experience in both global Fortune 500 companies and innovative startups.
“I am very pleased that Nina is joining our advisory board,” stated Tomasz Kulakowski, deepsense.ai’s chief executive officer. “We look forward to benefiting from her strong background in cutting-edge technologies, supporting our mission to improve people’s lives with machine learning-powered solutions. We welcome her strategic input and the combination of leadership, experience and industry knowledge she brings to deepsense.ai as the company transitions to the next phase of growth.”
Previous to NTT i3, Ms. Simosko was responsible for leading the creation and execution of Nike Technology strategy, planning and operations world-wide. Prior to that, she was Senior Vice President of SAP’s Global Premier Customer Network (PCN). At SAP, she led both the PCN Center of Excellence and SAP’s Global Executive Advisory Board. During her eight-year tenure, she was a part of SAP’s Global Ecosystem & Partner Group which was charged with continuing to build and enable an open ecosystem of software, service and technology partners together with SAP’s communities of innovation. Ms. Simosko currently sits on the Advisory board at Santa.io, AppOrchid and Reflektion and she has also been a member of the advisory boards at Appcelerator and Taulia.
“I am excited to join the company’s advisory board”, stated Ms. Simosko. “deepsense.ai is potentially solving one of the biggest challenges companies currently tackle in identifying, analyzing and solving problems with AI-powered solutions across multiple industries. I highly appreciate the company’s focus on research and their ability to deliver meaningful business results to their customers.” – Nina Simosko
“There is no development without research. To make our efforts in exploring new lands in machine learning more structured, deepsense.ai has launched its Research and Development Hub,” explains Tomasz Kułakowski, CEO of deepsense.ai.
deepsense.ai’s Research and Development Hub consists of four full-time researchers supported by a technical team. Supporting deepsense.ai’s business goals, the Hub has a fixed research budget funding projects that bridge the gap between enterprise and academia.
“Sometimes AI research projects seem distant from our everyday experience. But it’s actually just the opposite. The research we are doing today will power the autonomous cars and sophisticated prosthetic limbs of the future. Projects based on Atari games give us benchmarks on applying new techniques in computational-heavy projects done in more heavyweight simulators.” – Tomasz Kułakowski, CEO of deepsense.ai.
The team has conducted numerous successful research projects in cooperation with global leaders including Google Brain, Intel, a leading car manufacturer as well as top universities and scientific institutions around the world.
Key projects
deepsense.ai is currently focused on model-based reinforcement learning and transferring models from a simulated environment to the real world (sim2real). Reinforcement learning is a driving technology behind the large strides that have been made in AI, including recent success in Go, Chess,Dota 2 and StarCraft II.
Model-based reinforcement learning and learning in simulation address the challenge of collecting voluminous real-world data. That challenge does not exist in board games such as Go, Chess and e-sports, where one can collect an equivalent of millions of years of human gameplay and learn from such extensive datasets. It is, however, a major obstacle for real-world applications of reinforcement learning such as robotics.
In a recent project – Model-Based Reinforcement Learning for Atari – deepsense.ai set out to improve the quality of video models used in model-based reinforcement learning. Neural networks developed in this project are the state of the art in model-based reinforcement learning. The researchers were invited to present their work at Oxford University, Google Brain and the research company DeepMind.
Another project consists of training in Unreal Engine 4 and deployment on a real car. The objective of this research is to assess the feasibility of training a fully functional autonomous driving agent in simulation with only a minimal amount of real-world data. The project is being conducted in cooperation with a leading global car manufacturer.
“Long-term investment in research work is a distinguishing feature of deepsense.ai. Over the past three years, we have written a number of papers and presented at important ML venues including NeurIPS. All of this is done with a firm conviction in mind: machine learning is changing and developing so fast that today’s research can be tomorrow’s gold standard,” – Henryk Michalewski, head of deepsense.ai’s R&D team.
More of the research and development projects deepsense.ai has undertaken can be found on its recently published R&D Hub subsite.
Delivering the future
Apart from scientific work, the R&D Hub will make AI research more accessible and comprehensible for non-engineers and people who are unfamiliar with data science. “Our aim is to provide readers with as much information on our work as possible. Research should not be a hermetic game. It is about building a beneficial future,” comments Kułakowski. “I am proud that deepsense.ai is contributing to the development of this fascinating discipline.”
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Global e-commerce is among the fastest growing industries globally, experiencing 18% growth in 2018. Worldwide, consumers purchased $2.86 trillion worth of e-goods in 2018, compared to $2.43 trillion in 2017.
Because digital commerce is data-driven, the industry is ripe territory for AI. However, lack of knowledge and uncertainty remain the most prominent obstacles to this technology gaining a stronger foothold. To address these obstacles, deepsense.ai and Google Cloud co-organized a business breakfast to discuss the challenges and opportunities and share their remarks on artificial intelligence (AI) in e-commerce. Joining Google and deepsense.ai were experts from BeeCommerce.pl, Sotrender, and iProspect, all companies deliver sophisticated tools for digital business.
Plato’s data cave
“When it comes to building AI applications, it’s all about the data,” said Paweł Osterreicher, Director of Strategy & Business Development at deepsense.ai, during his presentation. He pointed out that the simplest analytics in smaller businesses can be done within an Excel spreadsheet or pen and paper. Preparing a simple segmentation within a client group or spotting best-performing products don’t pose a huge challenge. But those are only the tip of the iceberg. “The more sophisticated insights we gain, the more complicated the task becomes. And that’s where specialized software comes in,” he said.
“The greatest challenge is a lack of flexibility. There is no jack-of-all-trades among the popular tools, and each has its limitations. The problem is when a tool doesn’t fit a company’s needs. And, to be honest, that’s a common situation,” Osterreicher continued. Companies thus often need to tweak the tools at their disposal to make them fit or get used to missing insights from their data.
“Most companies process only a fraction of their data and operate with only half the picture. They are like the prisoners in Plato’s cave, watching only the shadows customers cast on the wall, with no access to or true grasp of their real form.”
The only way to analyze data in a convenient and cost-effective way is to leverage machine learning models. Machines are able to effectively spot patterns even in seemingly insignificant details.
“Sometimes information about how long customers hover over a button or how they go about filling in an online form is a first step to obtaining meaningful information. The model is only as good as the data it was built on,” concludes Osterreicher.
Retail reinvented
In another presentation, Jakub Skuratowicz focused on the technical aspects of how companies use AI. There are numerous ways for companies to benefit from AI, be it building engagement, personalizing the user experience or detecting fraud.
Google’s expert showed a new application of image search for omnichannel commerce. First applied by the Nordstrom clothing company, the app-enabled users to take a photograph of an item and then search for it in the shop’s database. Thus, the customer could quickly buy the product online or check its availability.
“By using Google Cloud Platform-delivered machine learning tools, the company reached 95% accuracy in recognizing an item shown in a photograph”
AI also thrives in recommendation engines. “It was common to recommend the user another version of the product – a different size of a dress, for example. That’s pointless. Why would one need another of the same dress, only slightly bigger?” Skuratowicz asked. Instead, the AI-powered model recommended products that complemented the one that had been searched for, like sunglasses or a scarf to go with the dress.
Skuratowicz also showed how AI spots fraudulent transactions in international e-commerce. “Manual or semi-automatic checking can be effective, but machine learning makes it more scalable,” he said. By applying AI-based solutions, the international logistics provider Pitney Bowes boosted the accuracy of its fraudulent transaction detection by 80% while reducing false-positives by 50%.
The mind barrier
The presentations were followed by a panel discussion on machine learning in e-commerce. As the panelists remarked, the AI-powered future of e-commerce is a challenge that not all companies are ready for.
In response to a question about the state of data-proficiency in e-commerce companies, Arkadiusz Wiśniewski, Director of Data and Technology at iProspect, had this to say:
“some data are easy to collect, while others provide a challenge, so we have an incomplete view. The legal situation in Europe poses an additional challenge, so it is better to focus on the data owned and make the best use of it.”
“Data-readiness depends to a great extent on company size. But most businesses lack the skills and data to effectively apply machine learning techniques,” agreed Jarosław Trybuchowicz, owner of beecommerce.pl.
The panelists agreed that the situation is hard even though data is becoming a commodity. “Sometimes the problem is the opposite. Despite having huge amounts of data, companies don’t get insights from it. They simply don’t know what questions to ask and what insights to look for,” added deepsense.ai’s Borys Sobiegraj.
The panelists likewise agreed that the key to success for enterprises employing machine learning is to know and properly organize their own data. Getting the data is the first challenge; “deciding what to do with it is a different story altogether,” said Jakub Nowacki, Lead Machine Learning Engineer at Sotrender. “Another challenge is extracting value that often lies in matching the data from different sources. If a company is unable to determine the impact of a sales campaign, then what is the purpose of analytics?” he added.
A question-answer session and networking time followed the discussion panel. The next business breakfast is planned for Q3.
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About deepsense.ai:
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https://deepsense.ai/wp-content/uploads/2019/05/shadows-of-customers-on-the-wall-key-takeaways-from-the-ai-in-e-commerce-business-breakfast.png7001920Konrad Budekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKonrad Budek2019-05-23 12:19:452019-06-03 13:28:56Shadows of customers on the wall - key takeaways from the “AI in e-commerce” business breakfast
deepsense.ai, Google Brain, the University of Warsaw and the University of Illinois at Urbana-Champaign have concluded a collaborative research project, building neural networks that mimic a simulated environment and effectively enabling artificial intelligence to perform a simulation.
Designed models can be applied in text or video prediction and to solve the problems where mathematical description may be overly complicated.
Reinforcement learning (RL) enables a neural network to develop and hone its skill by interacting with its environment, which is usually a simulated one. The technique is predicted to shine in robotics and the building of autonomous vehicles.
The research team found a way to build a neural network that can mimic the signals that the RL agent usually gets from interacting with the environment. The neural network produces signals that usually would be taken from sensors, like images. “This is one of the important ideas of reinforcement learning. A recent survey done by DeepMind’s J.B. Hammrick provides a thorough account of analogies between model-based reinforcement learning and mental simulation as considered by cognitive science”, says Henryk Michalewski, the R&D Coordinator at deepsense.ai and a visiting professor in the Computer Science Department of the University of Oxford.
The challenges of education
In the learning process, the RL agent is rewarded for performing a task correctly and punished for making mistakes. Autonomous cars provide perhaps the best example–the agent is rewarded for safe driving and punished for collisions and speeding. The model seeks to maximize the rewards and minimize the punishments. Initially, all actions are random and the neural networks have yet to explore the possible ways to perform the tasks. It is thus possible that more than the first dozen rides will end with a collision with the nearest wall as the model incurs punishment and learns how to brake or avoid it.
This leads to the idea of conducting the trial and error process inside a simulator.
However, building a realistic simulator is a tedious problem for humans– says Błażej Osiński, a Senior Data Scientist at deepsense.ai. For example, Unreal Engine 4 has several million lines of code. The idea of our project is to let the neural network learn how to simulate the environment. A similar approach was suggested in a recent work from Yann LeCun’s lab, where neural networks were employed to simulate dense traffic.
The research team has built a neural network that emulates the Atari gaming environment, a popular training ground for reinforcement learning models. The network was able to create a version of the games Pong, Freeway and others that were nearly indistinguishable from Atari’s.
Ongoing research
The project was run by data scientists and researchers from deepsense.ai including Henryk Michalewski, Piotr Miłoś, Błażej Osiński and fellow researchers from Google Brain, the University of Warsaw and the University of Illinois at Urbana-Champaign. More detailed information about the research, outcomes and possible uses can be found in the Arxiv paper and a detailed blogpost about artificial imagination on deepsense.ai’s blog.
The research opens up interesting ways to apply neural networks in business settings. The first is to build an RL agent that can explore a highly complex environment and then emulate it for the needs of models that are to perform tasks within it. The best example may be teaching the model to emulate the real world in all of its complexity and unpredictability.
Another business application would be a video prediction tool that could deliver videos based on only a few frames, effectively reducing the effort animators and designers put into producing full-length videos.
We consider our research work an essential part of deepsense.ai and a key aspect of the company’s development. This time our researchers have really pushed the boundaries of knowledge with no marketing overstatements. The project brings fresh, new idea into AI research, a thing that is worth contributing into.– concludes Tomasz Kułakowski, CEO at deepsense.ai.
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https://deepsense.ai/wp-content/uploads/2019/03/deepsense.ai-and-Google-Brain-design-artificial-imagination-for-reinforcement-learning.jpg7001920Konrad Budekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKonrad Budek2019-03-21 08:53:042019-07-14 19:07:31deepsense.ai and Google Brain design artificial imagination for reinforcement learning
deepsense.ai, an official Google Cloud Platform Training Partner, Tuesday hosted its first Business Breakfast: AI outlook for 2019 with participants from Google Cloud. A panel of experts discussed the development of technologies and trends to look forward to in 2019. There was a strong emphasis on cloud applications. The Breakfast officially kicked off the two companies’ collaboration.
The panelists shared their thoughts on the state of AI on the Polish market, the challenges facing it and the opportunities local business has for further development.
“Google has been working on AI-powered solutions for years and most of our tools are infused with it,” said Jakub Skuratowicz, Customer Engineer at Google during his presentation at the meeting. “Currently, more than 12% of Gmail responses use our automated response suggestions. Another example is our translation services, which were first tested as an internal tool but are now used by thousands of people around the world every day.” According to Skuratowicz, cloud-based AI solutions are not limited to daily operations in consumer services. Google’s Deepmind used AI to reduce the cooling bill for server rooms by more than 40%, and overall server room power consumption by 16%.
According to Gartner, the public cloud services market is projected to grow 17.3% in 2019, to $206.2 billion. That’s up from an already lofty $175.8 billion in 2018. The strongest growth, however, is forecast to occur in the Infrastructure as a Service segment, which is expected to grow 27.6% in 2019, to $39.5 billion. Building AI tools requires a great deal of computing power, and will be a serious component of that growth.
“Our goal is to make machine learning more accessible by applying it in the cloud,” adds Skuratowicz. “That can be achieved only by giving out reliable tools and delivering specialized infrastructure.”
“Artificial intelligence will not be a purpose in and of itself, but rather a sophisticated tool to solve business problems. But the data remains the greatest challenge and companies should start there,” said Paweł Godula, Director of Customer Analytics during his presentation of a Home Credit risk estimation tool. As the article makes clear, the estimation tool was clear proof that machine learning models are far better at scoring credit risk than human researchers.“Machines have no trouble analyzing high volumes of data and their perception is unlimited. Moreover, they cannot form presuppositions or be biased by previous experiences,” explains Godula. “Thus, they analyze data in a truly neutral way”. Godula believes the ability to get a holistic image of a single customer by connecting and cross-analyzing data from different sources is currently the pinnacle of artificial intelligence technology, and that’s where Google Cloud Platform thrives.
Fear of the black box
The discussion panel following the speeches was a great opportunity to exchange experience on the use of AI in both the public and private sectors.
“There is no doubt that artificial intelligence is fueling growth,” said Magdalena Dziewguć, Google Cloud Territory Manager. “If there are two tech firms, one of which supports its processes with AI while the other does things in a manual, old-fashioned way, there can be no doubt that the time and money the first saves will give it a better chance of surviving.”
“AI technology is becoming more convenient and common. Anti-spam filters or automated suggestions within search tools are considered a standard feature, not an AI-based power function. Even the most intelligent function becomes common after some time and users expect it to be a basic feature,” commented Robert Bogucki, deepsense.ai CTO. Both Rethink’s Borys Stokalski and Nielsen’s Paweł Bedyński agreed that AI is a problem-solving tool rather than just a fashionable buzzword.
When asked about the main obstacles and challenges for AI in getting more business attention, panelists named fear, doubt and uncertainty about new solutions emerging on the market.
“In its early days, digitalization once supported the status quo. But now legacy software and solutions are becoming more and more of a problem in various industries,” said Stokalski. “Startups are reinventing how business is done and the old models are being challenged.”
“Although some solutions have been present for years, the revolution is a matter of the last few years,” commented Bogucki. “Neural networks outperformed human experts in specialized tasks in 2015. Since then we have seen processes automated across a range of industries.”
The panelists had no doubt that AI solutions will become common and solve multiple challenges.
“Privacy issues are gaining greater notoriety and machine learning will become more private. We may be entering the era of tailored models serving particular customers and trained solely on their data, not the gargantuan datasets containing information about all the users of the product,” comments Bedyński.
“Moreover, the solutions are getting more affordable for business. Building mobile apps was expensive not so long ago. Now it is a common business channel. In my opinion, AI models will also become more widespread,” Bogucki added.
Business-focused meeting
The panel was followed by a networking session and it was an opportunity to exchange thoughts and remarks on machine learning applications in modern business. Participants from various industries asked about ways to solve the privacy and regulatory issues dogging AI and cloud applications.
“The lack of knowledge and courage to adopt machine learning models in daily business processes is the greatest obstacle companies face in leveraging this outstanding tool of growth,” explained Paweł Osterreicher, Business Development Director and host of the discussion panel. “But the more educational events spread the word, the more companies will find the strength to grab the opportunity.”
The meeting is the first in a series. To address the issues the panelists highlighted, the companies will be holding a three-day training session in February. More details about the event can be found on the Architecting with Google Cloud Platform course website.
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AI World is an artificial intelligence and machine learning conference being held in Boston, December 3-5. deepsense.ai will once again be providing its experience and expertise as a Strategic Deep Learning Partner at the conference.
AI World Conference and Expo annually gathers artificial intelligence (AI) professionals and practitioners to explore the state of practice of machine learning and deep learning in enterprise and the possible applications of this innovative technology. deepsense.ai thas been a strategic partner since the first conference and this year will be supporting the event for the third time.
The AI World conference is organized by AI Trends, the leading industry media channel focused on the business and technology of AI. Since its merger with Cambridge Innovation Institute, AI World has been focused on machine learning-based advances in the life sciences, especially for the healthcare and pharmaceutical industries. Eliot Weinman, AI World Conference Founder and Chair “AI World, now in our third year, has been collaborating with deepsense.ai to help us develop world-class seminars on the state of AI, machine learning and deep learning in the enterprise. deepsense.ai works hands-on with Fortune 500 class enterprises to help them deliver AI solutions and assist organizations in unlocking their data science potential. Their programs cover not only the state of the practice in machine learning and deep learning but also critical areas like cybersecurity.”
All the knowledge in one day
The conference will kick off with a range of thematic seminars, including one hosted by deepsense.ai on enterprise machine learning and deep learning. “Business leaders will hear about the latest developments in AI so they are up to date on what they can do and how they can assess their readiness to reach for AI innovation. However, this year we will focus more on the potential of machine learning for customer analytics, cybersecurity and healthcare”, shares Robert Bogucki, Chief Technology Officer in deepsense.ai and the host of one seminar. During the seminar, deepsense.ai experts will explain how to reduce human workloads with AI-based process automation and how to optimize revenues by applying automated customer analytics. The latter will be illustrated with a debt risk modeling prediction case done for Home Credit. Participants will also have the opportunity to join two discussions where panelists from leading organizations such as Microsoft, Uber, Nielsen and MIT will talk about AI applications in healthcare and cybersecurity. There are already numerous machine learning and deep learning solutions being used in healthcare, such as AI-powered diabetic retinopathy diagnostics, automated nurses, surgery assistance and personalized treatment plans. Experts will also show how AI can be also used for spotting the signs of DDoS attack before it downs a service, automated traffic analytics, advanced spam filtering technologies and fraud detection techniques. See here for a detailed agenda.
Opening the black box
“Artificial intelligence is a tool for building a new, brighter future. It offers easier access to healthcare services, it’s safer and just better for everyone. Still, there is a lot of uncertainty and doubt around artificial intelligence and how it is used. Companies don’t trust the solutions and ordinary people fear losing their jobs. That’s why we consider spreading knowledge about AI as important as building the solutions of the future,” says Bogucki. To address this challenge, AI World and deepsense.ai joined forces not only to popularize AI applications in research and business, but also to explaining how they work and how to start using them. AI Trend readers can read about modern recommendation systems, reinforcement learning development and potential use for the business and the latest advancements and challenges in NLP. They can also listen to a webinar on various approaches to using AI in business. After the conference, AI World and deepsense.ai will publish an e-book covering these topics. AI World Conference and Expo will be held in Boston, MA on December 3-5. The full agenda, speakers list and further details can be found on the official website. Use our special codes to get Free Expo Pass (code: 186800XDS) or save $200 on VIP Conference Pass (code: 186800DSAI).
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deepsense.ai has become a Polish Google Cloud Platform Authorized Training Partner. It is set to begin supporting the tech giant in training companies to build cloud-enabled AI solutions on their own. The two companies will also work together to deliver custom-tailored AI-related projects on GCP.
Building AI-based solutions requires enormous computing power. AI models need to process vast amounts of data to rise to the tasks they are designed for. Such tasks can include learning how to recognize particular objects depicted in photographs, like animals or vehicles or film stars. To gain access to the power required to perform these challenges while avoiding investment in expensive hardware, companies move to the cloud.
“The great benefit of using Google Cloud Platform is its integration with existing machine learning tools, including Google-designed Keras and TensorFlow frameworks”, says Paweł Osterreicher, Director of Strategy and Development at deepsense.ai. “The access we had to the Tensor Processing Unit, a specialized processor designed to augment and speed up the machine learning training process, was also crucial, as we were among the first companies to test it in action.”
This is not the first time deepsense.ai and Google have teamed up. The Polish company worked with Google Brain on reinforcement learning, which uses an AI agent to learn not by analyzing data provided by data scientists, but by interacting with the environment. The technique can be used to design software for autonomous cars, to name one prominent example. The companies’ cooperation has also resulted in scientific papers on research deepsense.ai and Google Brain conducted in conjunction with Warsaw University.
AI in the clouds
Until now, deepsense.ai has shared its knowledge through training programs on data science and artificial intelligence. Teaching the practical use of Google Cloud Platform, the training enabled users to learn about tools for building machine learning and deep learning models as well as the cloud infrastructure required to do the computations.
“The CEE cloud computing market is growing rapidly, but it continues to lag western countries,” says Magdalena Dziewguć, Google Cloud Territory Manager. “The lack of cloud computing competencies is the greatest obstacle to overcome. With deepsense.ai as our authorized training partner, we’ll be able to tackle this challenge by providing companies from Central and Eastern Europe with the necessary knowledge provided in an effective and accessible way.
In the new partnership, deepsense will introduce a five-day training to help participants become familiar with basic machine learning techniques and tools and the basics of deep neural networks. Certified instructors will also show how to use Google Cloud Platform in machine learning.
“We will be addressing the training to companies that feel machine learning can benefit their business, but which lack experience”, comments Marta Piechowiak, Head of the Training and Development Hub at deepsense.ai. “A key challenge in the development of artificial intelligence is to help companies build the requisite competencies. deepsense.ai can use its experience in training and implementation to support both individuals and companies in their development. Cooperation with Google broadens our data science training portfolio with the new possibilities Google cloud affords.”
deepsense.ai is also now an authorized Google service partner. The company will support Google’s customers with tailored AI-based solutions. The partnership will allow deepsense.ai to share its experience in image recognition, predictive analytics and natural language processing with Google’s clients. Combining that experience with Google Cloud Platform will enable companies to reach for AI without the need to invest in expensive hardware or build out a dedicated infrastructure.
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The game “Museum Treasures” created by the DS team, including several deepsense.ai employees, took first place in the National Museum in Warsaw’s HackArt hackathon. The game was on display at the Przemiany Festival, where this year’s leitmotif was artificial intelligence.
“The National Museum in Warsaw and hackathon is not a combination one sees every day, which makes it interesting”, muses Agata Chęcińska, a data scientist at deepsense.ai and member of the DS team.
In hackathons, teams of programming, analysis, creation and research specialists produce innovative solutions in a limited time. These competitions can be organized by companies, state administrations, NGOs and, increasingly, public institutions. In May, the National Museum in Warsaw got in on the act, organizing its programming marathon with a focus on artificial intelligence.
Hunting down the museum’s treasures
The game “Museum Treasure – find hidden details in the paintings!” is designed for parents and children, who can choose their favorite paths through the museum, with names like “animals” or “body parts”, to find their favorite ones. The application brings up fragments of well-known works pre-selected by AI from a digitalized collection housed in the museum. Working by category, the algorithm looks for hands, types of clothes, cuisine or any number of other objects in the paintings. Children then go and find them in the National Museum gallery that houses the items featured. In addition to being good fun, each path awards a badge and prize to those who finish, which will encourage people to visit the museum again and keep discovering new corners. The game is cut from the same mold as PokemonGo. In this case, instead of creatures in the city, participants seek out specific fragments of images within the museum.
“Artificial intelligence helped in selecting the appropriate fragments from among the museum’s collections”, explains Agata Chęcińska, a data scientist at deepsense.ai. “Thanks to the challenge, children will look carefully at the pictures, and thanks to the good fun they’ll remember more”.
Future versions of the application will allow visitors to play individually, or in groups that can then compare the time it took members to find all of the treasures.
“HackArt MNW was the first hackathon organized by an art museum in Poland with the help of our partner, the Polish Development Fund Foundation. 40 participants and over 20 mentors took part. The projects submitted during the hackathon exceeded our expectations, with their creativity and advanced use of technology”, says Ewa Drygalska, of the museum’s Communications and Public Programs Department, and coordinator of HackArt MNW. “The ‘Museum Treasures’ project captivated us and the jurors because it helps our visitors explore the museum’s collections in an interesting and original way”.
Transforming art
A demo version of the game was available in the AI Showroom at the Przemiany Festival. The theme of this year’s festival was artificial intelligence and its impact on people’s everyday lives.
“There is a lot of emotion surrounding AI, particularly with questions about its safety and role in society”, explains Chęcińska. “Our application shows that in many cases such fears are unjustified, because AI is only playing the role of servant and the real value is rooted elsewhere. This is an excellent example of how AI supports and develops an institution that may not be associated with innovation. I hope that thanks to the use of our application, children will see the National Museum in Warsaw as a fun place, and that visiting it will be perceived as a great way to spend time on the weekend”, Chęcińska says.
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CEO sees participation in Dubai conference as great way to begin executing company’s recently announced five-year business strategy.
To take place in the Hyatt Park Dubai Conference Center on September 4th and 5th, the AI Week Middle East conference will bring together AI practitioners and specialists building real-life solutions, primarily in the banking, healthcare and public sectors. deepsense.ai’s experience and expertise earned the company an invitation to join the event as a strategic partner.
According to deepsense.ai CEO Tomasz Kulakowski, “The conference will be a timely opportunity to share our views on both the current state of AI and the trends that are yet to come. Dubai is a fantastic place to do this, as the city is showing today that AI is going to revolutionize both business and people’s daily lives”.
AI in the U.A.E. – a glimpse into the future
The UAE’s prime minister last year appointed Omar Bin Sultan Al Olama the country’s—and the world’s, no less—first Minister of State for Artificial Intelligence. Wasting no time, Al Olama signed a Memorandum of Understanding with Deepak Bagla, the managing director and CEO of Invest India, to create a bilateral Artificial Intelligence Bridge between the countries. The initiative is expected to bring $20 billion in economic benefits over the next decade. The federation also recently announced its broader Artificial Intelligence Strategy, making AI the first project run as a part of Centennial Plan 2071, which covers the country’s vision of development. The AI strategy is one pillar in the government’s effort to boost GDP by 35% and reduce spending by some 50% by 2031. The UAE intends to use AI to boost its education, energy, transportation, technology and aerospace sectors.
“We are excited about the steps Dubai is taking, as the country turns science-fiction into reality”, says Paweł Osterreicher, Director of Strategy and Business Development at deepsense.ai. “With uncertainty and doubt among the main obstacles to wider scale adoption of AI, the UAE is truly showing the way. And by appointing its AI minister and forging a feasible strategy, the country is on the front edge of the wave that is going to change the world. We at deepsense.ai aim to help them stay there by extending our technological and business expertise for their AI projects”.
With deepsense.ai’s recently announced five-year strategy to become the Leading European AI Powerhouse, building a position in the Middle East provides strong complementary business opportunities and makes excellent strategic sense. Participating in the conference is only a first step, as deepsense.ai joins the likes of Microsoft, Accenture, PwC and General Electric as conference partners. For the full conference agenda, see the official website.
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deepsense.ai researcher Henryk Michalewski is set to join Michael Benedikt’s lab as a visiting professor at the Department of Computer Science at Oxford University. Michalewski’s post will begin September 1.
deepsense.ai is committed to contributing to international-level research in deep learning and reinforcement learning. Last year our researchers created a reinforcement learning agent that learned how to run like a human, using muscles and a skeleton. It also parallelized training in Atari games so that it scales almost linearly up to 64 nodes. deepsense.ai has recently published a five-year strategy to become a European AI powerhouse, which will see it invest heavily in R&D, particularly reinforcement learning while continuing to focus on image recognition and natural language processing.
“We see reinforcement learning as a technology of the future and that’s why we focus on it in our research. It can reshape the landscape in various ways. For instance, we are currently applying it in our work on autonomous vehicles. There’s no challenge in following the trail blazers. Real technological leadership means being the trail-blazer”, comments Tomasz Kułakowski, CEO of deepsense.ai.
Both students and researchers at Oxford have successfully launched startups providing innovative and interesting AI-based services. Among them is the well-known DiffBlue, which uses AI to support programmers in the most repetitive tasks, including code debugging and software testing. Student-founded Onfido performs identity background checks by scrapping public databases and the data available online to make HR and hiring tasks easier. Other interesting startups showing out-of-the-box thinking include IntelligentX, the AI powered brewery and OpenMined, which builds open-source and transparent AI. “With his inquiring nature and expertise in AI, Henryk will be able both to support Oxford University’s research on AI and share his knowledge with students”, said deepsense Chief Technology Officer Robert Bogucki. “Oxford is a great destination for a researcher and I am proud that deepsense can contribute to it”.
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About deepsense.ai:
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https://deepsense.ai/wp-content/uploads/2019/02/deepsense.ai’s-Henryk-Michalewski-to-join-Oxford-University-as-a-Visiting-Professor.jpg3371140Konrad Budekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgKonrad Budek2018-08-23 08:36:412019-06-03 13:28:57deepsense.ai's Henryk Michalewski to join Oxford University as a Visiting Professor
