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When asking around to find the coolest startups in Helsinki, Curious AI was put forward. The company does not disappoint even its approach to being a startup is unique.
Curious AI is a research-based company founded 2015 as a spin-off from Aalto's Deep Learning Research Group. While conventional startups are trying to find profitable applications for the current state of technology, Curious AI is interested in something very different.
Other companies are using machine learning to solve problems now. We are driving the scientific development towards the next level of AI, explains Antti Rasmus, CTO of Curious AI.
Curious AI does cutting edge research, pushing the boundaries of the machine learning commonly called artificial intelligence today towards the illusive limit of true artificial intelligence. The proprietary patents the research leads to are the future incomes of Curious AI a business model similar to pharmas, with long risky development, but a big payoff at the end.
And like for biotech companies, there are some very big competitors trying to get there first. Curious AIs main competitors all have big academic and/or financial muscles. They are the tech giants all working on AI independently: Facebook, Googles Deepmind, Microsoft, etc. along with academic institutions and universities. So far, however, Curious AI is very much in the race.
Curious AIs results are state of the art.
Curious AI recently had a breakthrough, using unsupervised learning for object recognition in Google Street View.
What research team has come the furthest is difficult to say, considering everyone is working on different aspects of the problem, but Antti Rasmus does say this,
Our results are on par with the best in the world. Considering were a small independent group, its a pretty big achievement that Curious AIs is among the top three in the field.
To make this possible, Curious AI has a secret weapon in CEO Harri Valpola. A Finnish math prodigy, he was fascinated with brains from an early age and went on to study machine learning, theoretical neuroscience and artificial intelligence. Leading a research team at Aalto University led to Valpola co-founding ZenRobotics, which uses machine learning for robotic waste separation. So when he founded Curious AI to commercialise on the results of another research group he led, you could say hed been preparing for it pretty much his whole life.
The rest of Curious AIs team primarily consists of PhDs. It does, of course, also require substantial funding from global future-oriented VCs. Curious AI's current backers are Lifeline, Balderton and Invus.
Unsupervised learning is a major milestone for achieving true artificial intelligence.
he term artificial intelligence is thrown around a lot these days, but usually, when a startup says theyre applying AI to some problem, it just means they are using machine learning in varying degrees of sophistication. And there is a big difference. Not to say machine learning doesnt have huge potential for automation and optimizing computer responses to various problems, but artificial intelligence is at a completely different level.
A major obstacle to reaching artificial intelligence is solving unsupervised learning and this is what Curious AIs primary focus.
AI researchers have gotten very good at applying machine learning to the essential task of object recognition in images accuracy now actually exceeds humans. The problem is that usually a machine has to be trained with a huge set of images tagged with different categories in order to learn to recognize those objects in other images.
A human brain doesnt work like that, categories (concepts and objects) are formed without anyone pointing and saying Thats a cat, This is a dog. This is the goal of unsupervised machine learning, letting a computer form the categories itself as it learns to recognize them. Thats an essential task if computers are ever to be able to solve problems independently.
In one example, Curious AI used unsupervised learning to reduce the number of manual tags (nominations of objects in pictures) from 70,000 to 500 which saves quite a bit of work. For smaller companies that haven't been gathering their own data and labelling it for years or decades, any reduction in the amount of tags needed means lowering the threshold for applying macine learning, so it is extremely valuable.
Another big success of Curious AI is to use perceptual grouping - a way of clustering information. Other image recognition approaches may well be able to recognize that theres a car in a picture, but not be able to infer that there are in fact two cars. Curious AI clusters related pixels together, so that one car can be recognized as a separate object from another.
This also addresses the difficult problem of understanding layered images. It can be quite confusing for a computer to recognize a car obscured by a fence or tree so that only parts are showing. Curious AIs approach lets the computer infer that there is a fence in front of a car or two cars instead of recognizing just stripes of car. For self-driving technology, being able to infer that one object is behind another is of course invaluable.
This video shows what Curious AI are up to against the background of AI's historical development, have a look:
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During the International Conference on Learning Representations (ICLR) 2020 this week, which as a result of the pandemic took place virtually, Turing Award winner and director of the Montreal Institute for Learning Algorithms Yoshua Bengio provided a glimpse into the future of AI and machine learning techniques. He spoke in February at the AAAI Conference on Artificial Intelligence 2020 in New York alongside fellow Turing Award recipients Geoffrey Hinton and Yann LeCun. But in a lecture published Monday, Bengio expounded upon some of his earlier themes.
One of those was attention in this context, the mechanism by which a person (or algorithm) focuses on a single element or a few elements at a time. Its central both to machine learning model architectures like Googles Transformer and to the bottleneck neuroscientific theory of consciousness, which suggests that people have limited attention resources, so information is distilled down in the brain to only its salient bits. Models with attention have already achieved state-of-the-art results in domains like natural language processing, and they could form the foundation of enterprise AI that assists employees in a range of cognitively demanding tasks.
Bengio described the cognitive systems proposed by Israeli-American psychologist and economist Daniel Kahneman in his seminal book Thinking, Fast and Slow. The first type is unconscious its intuitive and fast, non-linguistic and habitual, and it deals only with implicit types of knowledge. The second is conscious its linguistic and algorithmic, and it incorporates reasoning and planning, as well as explicit forms of knowledge. An interesting property of the conscious system is that it allows the manipulation of semantic concepts that can be recombined in novel situations, which Bengio noted is a desirable property in AI and machine learning algorithms.
Current machine learning approaches have yet to move beyond the unconscious to the fully conscious, but Bengio believes this transition is well within the realm of possibility. He pointed out that neuroscience research has revealed that the semantic variables involved in conscious thought are often causal they involve things like intentions or controllable objects. Its also now understood that a mapping between semantic variables and thoughts exists like the relationship between words and sentences, for example and that concepts can be recombined to form new and unfamiliar concepts.
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Attention is one of the core ingredients in this process, Bengio explained.
Building on this, in a recent paper he and colleagues proposed recurrent independent mechanisms (RIMs), a new model architecture in which multiple groups of cells operate independently, communicating only sparingly through attention. They showed that this leads to specialization among the RIMs, which in turn allows for improved generalization on tasks where some factors of variation differ between training and evaluation.
This allows an agent to adapt faster to changes in a distribution or inference in order to discover reasons why the change happened, said Bengio.
He outlined a few of the outstanding challenges on the road to conscious systems, including identifying ways to teach models to meta-learn (or understand causal relations embodied in data) and tightening the integration between machine learning and reinforcement learning. But hes confident that the interplay between biological and AI research will eventually unlock the key to machines that can reason like humans and even express emotions.
Consciousness has been studied in neuroscience with a lot of progress in the last couple of decades. I think its time for machine learning to consider these advances and incorporate them into machine learning models.
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Yoshua Bengio: Attention is a core ingredient of conscious AI - VentureBeat
To close the gender pay gap, we cant start by talking about pay. And thats where Roys SaaS platform Pipeline steps in.
Inequality in the workplace has long been a hot topic in the corporate world, but gender economist Katica Roy is actively turning that conversation into action. After fighting to be paid equitably twiceand winningRoy was on a radio show when asked if she thought the pay gap would be closed in her lifetime.
I said, Well not until we make it an economic issue. And then I thought, oh, I think I can solve that. Roy explained to Worth CEO Juliet Scott-Croxford, during the first day of the weeklong Techonomy Virtual: Reset + Restore conference. So, that was my entry into entrepreneurship and really looking at gender equity as a massive economic opportunity rather than only a social issue.
This realization led Roy to found Denver-based Pipeline Equity, an award-winning SaaS company that leverages artificial intelligence (AI) to identify and drive economic gains through gender equity.
The idea behind [Pipeline and its application of AI] was: If we could change decisions that are made in organizations, the human capital decisions, we could actually make gender equity a reality in our time in our lifetime, rather than the hundred or so years that the World Economic Forum (WEF) talks about. Roy said. But also, we could essentially augment the decision making. We could also change the narrative from gender equity as this social issue to an economic opportunity. The thing that we also saw more broadly in the marketplace was that there was an increasing number of CEOs who were committed to gender equity and have made these public commitments. Yet, there was really a difference between making a commitment and living a commitment, really the difference between making that commitment and actually the employee experience of that commitment.
So, if we could change the way that the decisions are made, she continued, then we could actually catapult that opportunity forward.
Of course, the WEF pay gap closure predictions were made before the COVID-19 pandemic hit, which Roy called a she-session, because women have lost the majority of jobs during this particular economic downturn.
With remote work and with people being at home with children, most caregiving and unpaid work falls to women, Roy explained. Weve seen that theres about 40 percent of U.S. employees [who] dont have sick leave. So, they have to choose between their health and going to work. And most of that actually falls on womenthis particular economic downturn has really highlighted all the cracks that were already sitting underneath the system.
But while this crisis has been a giant step backward for equality, at the same time, what we are seeing is this opportunity to actually use artificial intelligence and use people analytics solutions to catapult us forward, Roy said.
With the move to remote work, there has been a lot of talk about the flexibility of such situations being good for gender equity, but Roy warned that while working from home could help keep more women in the workforce, it could have a negative impact on female promotions.
At this moment, theres the opportunity to actually leverage artificial intelligence to ensure that we are not only continuing to make progress on gender equity, but actually catapulting us forward, Roy explained. And for companies, that is of particular importance because in the 2008 Great Recession, what we saw was companies that put equity at the core of their crisis management strategy actually increased the velocity of their recovery. So, for companies that are looking to recover, and recover more quickly, from this particular downturn, gender equity is important.
But to close the gender pay gap, we cant start by talking about pay. Pay is the symptom, its not the disease, Roy said. In other words, pay is the quantitative value that you place on your talent. But the actual value happens before that in performance and potential. And thats where Pipelines platform steps in.
We are augmented decision-making, Roy, who also acts as Pipelines CEO, said. So much like you would use Google Maps or ways to get from point A to point B, we do the very same thing for companies human capital management decisions. Essentially, its their data, our algorithms. We attach to their HR systems when theyre going to make a decision across these five pillars of talent, which essentially are kind of the five big buckets of talent decisions you make: hiring, pay, performance, potential and promotion. We actually intercept those decisions, essentially audit them. And then if theres any inequity, make a recommendation.
Using natural language processing, Pipelines platform reads through performance reviews and calls out any biased phrases. Weve found that on average, women are underrated 4 percent of the time, and that actually impacts their ability to be in the succession pipeline, as well as their pay. So thats what we make possible. Just to give you one more quick stat: The average Fortune 500 company has 60,000 employees. And what we have discovered is that theres really three key decisions that they make across those employees each year, which are performance, potential and pay. So, thats 180,000 opportunities for the average Fortune 500 company to move toward equity each and every year. Thats what we make possible.
According to Roy, in the next five years, we could reduce the time it will take to tackle gender equity, which is currently estimated at 257 years. We could actually shorten that quite a bit, Roy said. At this moment, we have the opportunity to embrace AI as a tool to achieve gender equity.
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How Katica Roy Is Using AI to Bring Equality Into the Workplace - Worth
DUBLIN, Dec. 17, 2019 /PRNewswire/ -- The "AI Solutions and Market Opportunities: AI & Cognitive Computing Technologies, Infrastructure, Capabilities, Leading Apps, and Services (2019-2024)" report has been added to ResearchAndMarkets.com's offering.
This is the most comprehensive research available covering artificial intelligence in telecommunications, media, and digital technology as a whole. For example, it covers AI in everything from consumer devices to communications networks as well as AI in key markets and technologies such as AI in supply chain management and AI-bases smart machines used in various industry verticals. It also covers the convergence of AI with IoT (AIoT), which is also known as the Artificial Intelligence of Things. It also provides a look ahead towards general-purpose intelligence, which represents the evolution of AI towards a utility function in which cognitive capabilities are leveraged within virtually every product, service, application, and solution.
One of the fastest-growing areas for artificial intelligence is the AI chipset marketplace, which is poised to transform the entire embedded system ecosystem with a multitude of AI capabilities such as deep machine learning, image detection, and many others. This will also be transformational for existing critical business functions such as Identity management, authentication, and cybersecurity. Multi-processor AI chipsets learn from the environment, users, and machines to uncover hidden patterns among data, predict actionable insight, and perform actions based on specific situations. AI chipsets will become an integral part of both AI software/systems as well as critical support of any data-intensive operation as they drastically improve processing for various functions as well as enhance overall computing performance.
For example, AI-enabled chatbots are taking Customer Relationship Management (CRM) to a new level as business-to-business, business-to-consumer, and consumer-to-business communications is both automated and improved by way of push and pull of the right information at the right time. Chatbots also provide benefits to customers as both existing clients and prospects enjoy the freedom to interact on their own terms. Our research indicates that over 50% of customer queries may be managed today via AI-based chatbots. As the interface between humans and computers evolves from an "operational" interface (Websites and traditional Apps) to an increasingly more "conversational" interface expectations about how humans communicate, consume content, use apps, and engage in commerce will change dramatically. This transformation is poised to impact virtually every aspect of marketing and sales operations for every industry vertical. For example, AI-enabled voice chat, also known as conversational AI, provides a completely human-like experience and will completely replace human-based CRM in some industries.
Smart machines collectively represent intelligent devices, machinery, equipment, and embedded automation software that perform repetitive tasks and solve complex problems autonomously. Along with AI, IoT connectivity, and M2M communications, smart machines are a key component of smart systems, which include many emerging technologies such as smart dust, neuro-computing, and advanced robotics. Smart machines will also benefit significantly from advancements in AIoT. The drivers for enterprise and industrial adoption of smart machines include improvements in the smart workplace, smart data discovery, cognitive automation, and more. Currently conceived smart machine products include autonomous robots (such as service robots), self-driving vehicles, expert systems (such as medical decision support systems), medical robots, intelligent assistants (such as automated online assistants), virtual private assistants (Siri, Google Assistant, Amazon Alexa, etc.), embedded software systems (such as machine monitoring and control systems), neurocomputers (such as purpose-built intelligent machines), and smart wearable devices.
Computing at the edge of IT and communications networks will require a different kind of intelligence. AI will be required for both security (data and infrastructure) as well as to optimize the flow of information in the form of streaming data and the ability to optimize decision-making via real-time data analytics. Edge networks will be the point of the spear so to speak when it comes to data handling, meaning that streaming data will be available for processing and decision-making. While advanced data analytics software solutions can be very effective for this purpose, there will be opportunities to enhance real-time data analytics by way of leveraging AI to automate decision making and to engage machine learning for ongoing efficiency and effectiveness improvements.
Cognitive informatics is poised to become an important aspect of every major vertical. The cognitive informatics market relies upon those technologies that improve human information processing. Technologies included within this interdisciplinary domain always include some degree of Artificial Intelligence and cognitive computing, but are increasingly involving Internet of Things (IoT) enabled devices, networks and systems. In fact, this multidisciplinary combination of cognition and information sciences includes the convergence of AI and IoT, which is also referred to as the AIoT market. As human beings have cognitive limitations (such as attention, comprehension, decision-making, learning, memory, learning, and visualization), the cognitive informatics market seeks to provide human cognition augmentation and enhancement. Advancements in the understanding human behavioral science, neuroscience, and psychology are combined with innovation in AI such as improved Natural Language Processing (NLP) mechanisms and linguistics processes. Machine learning improvements to areas such as what is said vs. what is meant and context-based AI are leading to an overall improvement in man-machine interfaces critical to successful cognitive informatics market implementation.
The role and importance of AI in 5G ranges from optimizing resource allocation to data security and protection of network and enterprise assets. However, the concept of using AI in networking is a relatively new area that will ultimately require a more unified approach to fully realize its great potential. In addition, AI will assist 5G network slicing, which represents the ability to dynamically allocate bandwidth, and enforce associated service level agreements, and a per-customer and per-application basis. AI will automate the process of assigning network slices, including informing enterprise customers when the slices they are requesting are not in their best interest based on anticipated network conditions.
The convergence of AI and Internet of Things (IoT) technologies and solutions (AIoT) is leading to thinking networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals. AI adds value to IoT through machine learning and improved decision making. IoT adds value to AI through connectivity, signaling, and data exchange. AIoT is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination. With AIoT, AI is embedded into infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks.
APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data. While early AIoT solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes.
IoT in consumer, enterprise, industrial, and government market segments has very unique needs in terms of infrastructure, devices, systems, and processes. One thing they all have in common is that they each produce massive amounts of data, most of which is of the unstructured variety, requiring big data technologies for management. AI algorithms enhance the ability for big data analytics and IoT platforms to provide value to each of these market segments. The author sees three different types of IoT Data: (1) Raw (untouched and unstructured) Data, (2) Meta (data about data), and (3) Transformed (valued-added data). AI will be useful in support of managing each of these data types in terms of identifying, categorizing, and decision making.
AI coupled with advanced big data analytics provides the ability to make raw data meaningful and useful as information for decision-making purposes.The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service.
Experiential networking is a relatively new concept with ETSI forming an Industry Specification Group (ISG) focused on Experiential Network Intelligence (ENI) and holding an initial ISG ENI meeting in 2017. These efforts define an observe-orient-decide-act control model with the intent that networks will become increasingly more adaptive, supporting intelligent service operations by way of cognitive network management. Accordingly, core to the experiential networking market is the use of AI and cognitive computing. More specifically, ENI will leverage data and contextual information (such as AI-based decision making) to take actions based on device and system-related events. Responses to events, related processes, and machine learning, allows ENI to make automated decisions and provide recommendations for use by other systems such as management and orchestration platforms. This event-driven approach allows the experiential networking market to use various technologies to engage in intelligent analysis necessary for network and service policies and modeling.
Also known as Artificial General Intelligence (AGI), General Purpose Artificial Intelligence represents silicon-based AI that mimics human-like cognition to perform a wide variety of tasks that span beyond mere number crunching. Whereas most current AI solutions are limited in terms of the type and variety of problems that may be solved, AGI may be employed to solve many different problems including machine translation, natural language processing, logical reasoning, knowledge representation, social intelligence, and numerous others. Unlike many early AI solutions that were designed and implemented with a narrow focus, AGI will be leveraged to solve problems in many different domains and across many different industry verticals including 3D design, transforming customer service, securing enterprise data, securing public facility and personnel, financial trading, healthcare solution, highly personalized target marketing, detecting fraud, recommendation engines, autonomous vehicles and smart mobility, online search, and many other areas. AGI is rapidly evolving in many areas. However, scalability remains a challenge.
Modern supply chains represent complex systems of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer. Supply Chain Management (SCM) solutions are typically manifest in software architecture and systems that facilitate the flow of information among different functions within and between enterprise organizations. Leading SCM solutions catalyze information sharing across organizational units and geographical locations, enabling decision-makers to have an enterprise-wide view of the information needed in a timely, reliable and consistent fashion. Various forms of AI are being integrated into SCM solution to improve everything from process automation to providing greater visibility into static and real-time data as well as related management information systems. In addition to fully automated decision making, AI systems are also leveraging various forms of cognitive computing to optimize the combined efforts of artificial and human intelligence. For example, AI in SCM is enabling improved supply chain automation through the use of virtual assistants, which are used both internally (within a given enterprise) as well as between supply chain members (e.g. customer-supplier chains).
Report and Topics Covered:
Artificial Intelligence Market by Technology, Infrastructure, Components, Devices, Solutions, and Industry Verticals 2019-20241. Introduction2. Overview3. Technology Impact Analysis4. Market Solutions and Applications Analysis5. Company Analysis6. AI Market Analysis and Forecasts 2019-20247. Conclusions and Recommendations
Artificial Intelligence of Things: AIoT Market by Technology and Solutions 2019-20241. Executive Summary2. Introduction3. AIoT Technology and Market4. AIoT Applications Analysis5. Analysis of Important AIoT Companies6. AIoT Market Analysis and Forecasts 2019-20247. Conclusions and Recommendations
Artificial Intelligence in Big Data Analytics and IoT: Market for Data Capture, Information and Decision Support Services 2019-20241. Executive Summary2. Introduction3. Overview4. AI Technology in Big Data and IoT5. AI Technology Application and Use Case6. AI Technology Impact on Vertical Market7. AI Predictive Analytics in Vertical Industry8. Company Analysis9. AI in Big Data and IoT Market Analysis and Forecasts 2019-202410. Conclusions and Recommendations11. Appendix
Artificial Intelligence (AI) in Supply Chain Management (SCM) Market: AI in SCM by Technology, Solution, Management Function (Automation, Planning and Logistics, Inventory, Fleet, Freight, Risk), and Region 2019-20241. Executive Summary2. Introduction3. AI in SCM Challenges and Opportunities4. Supply Chain Ecosystem Company Analysis5. AI in SCM Market Analysis and Forecasts 2019-20246. Summary and Recommendations
AI based Chatbot Market by Type (Text, Voice, and Hybrid), Use Case, Deployment Type, Value Chain Component, Market Segment (Consumer, Enterprise, Industrial, Government), Industry Vertical, Region and Country 2019-20241. Executive Summary2. Introduction3. Intelligent Chatbots Ecosystem Analysis4. Chatbot Market: SWOT Analysis and Use Cases5. Chatbot Company and Solution Analysis6. Conclusions and Recommendations7. AI Based Chatbot Market Analysis and Forecasts 2019-20248. Regional AI based Chatbot Market 2019-20249. Conversational AI Forecasts 2019-2024
Smart Machines in Enterprise, Industrial Automation, and IIoT by Technology, Product, Solution, and Industry Verticals 2019-20241. Introduction2. Smart Machine Ecosystem3. Smart Machine Market Analysis and Forecasts4. Company Analysis5. Conclusions and Recommendations6. Appendix: General Purpose AI Market Analysis and Forecasts
Artificial General Intelligence (AGI) Market: General Purpose Artificial Intelligence, AI Agent Platforms, and Software1. Executive Summary2. Introduction3. Technology and Application Analysis4. General Purpose AI Market Analysis and Forecasts5. Company Analysis6. Conclusions and Recommendations
Experiential Networking Market by Technology, Use Case, and Solutions 2019-20241. Executive Summary2. Overview3. Introduction4. Technologies Supporting Experiential Networking5. Company Analysis6. Experiential Networking Market Analysis and Forecasts 2019-20247. Conclusions and Recommendations
AI Chipsets for Wireless Networks and Devices, Cloud and Next Generation Computing, IoT, and Big Data Analytics 2019-20241. Executive Summary2. Research Overview3. AI Chipsets Introduction4. Technologies, Solutions, and Markets5. Company Analysis6. AI Chipsets Market Analysis and Forecasts 2019-20247. Conclusions and Recommendations
Cognitive Informatics Market by Technology, Solution (Smart Data, Self-Adaptive Software, Self-Correcting Infrastructure, Cognitive Analytics), Sector (Consumer, Enterprise, Industrial, Government), Industry Vertical, and Region 2019-20241. Executive Summary2. Introduction3. Technologies and Applications4. Company Analysis5. Cognitive Informatics Market Analysis and Forecasts6. Conclusions and Recommendations
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In the future, its likely that many aspects of human society will be controlled either partly or wholly by artificial intelligence. AI computer agents could manage systems from the quotidian (e.g., traffic lights) to the complex (e.g., a nations whole economy), but leaving aside the problem of whether or not they can do their jobs well, there is another challenge: will these agents be able to play nice with one another? What happens if one AIs aims conflict with anothers? Will they fight, or work together?
Googles AI subsidiary DeepMind has been exploring this problem in a new study published today. The companys researchers decided to test how AI agents interacted with one another in a series of social dilemmas. This is a rather generic term for situations in which individuals can profit from being selfish but where everyone loses if everyone is selfish. The most famous example of this is the prisoners dilemma, where two individuals can choose to betray one another for a prize, but lose out if both choose this option.
As explained in a blog post from DeepMind, the companys researchers tested how AI agents would perform in these sorts of situations, by dropping them into a pair of very basic video games.
In the first game, Gathering, two player have to collect apples from a central pile. They have the option of tagging the other player with a laser beam, temporarily removing them from the game, and giving the first player a chance to collect more apples. You can see a sample of this gameplay below:
In the second game, Wolfpack, two players have to hunt a third in an environment filled with obstacles. Points are claimed not just by the player that captures the prey, but by all players near to the prey when its captured. You can see a gameplay sample of this below:
What the researchers found was interesting, but perhaps not surprising: the AI agents altered their behavior, becoming more cooperative or antagonistic, depending on the context.
For example, with the Gathering game, when apples were in plentiful supply, the agents didnt really bother zapping one another with the laser beam. But, when stocks dwindled, the amount of zapping increased. Most interestingly, perhaps, was when a more computationally-powerful agent was introduced into the mix, it tended to zap the other player regardless of how many apples there were. That is to say, the cleverer AI decided it was better to be aggressive in all situations.
AI agents varied their strategy based on the rules of the game
Does that mean that the AI agent thinks being combative is the best strategy? Not necessarily. The researchers hypothesize that the increase in zapping behavior by the more-advanced AI was simply because the act of zapping itself is computationally challenging. The agent has to aim its weapon at the other player and track their movement activities which require more computing power, and which take up valuable apple-gathering time. Unless the agent knows these strategies will pay off, its easier just to cooperate.
Conversely, in the Wolfpack game, the cleverer the AI agent, the more likely it was to cooperate with other players. As the researchers explain, this is because learning to work with the other player to track and herd the prey requires more computational power.
The results of the study, then, show that the behavior of AI agents changes based on the rules theyre faced with. If those rules reward aggressive behavior (Zap that player to get more apples) the AI will be more aggressive; if they rewards cooperative behavior (Work together and you both get points!) theyll be more cooperative.
That means part of the challenge in controlling AI agents in the future, will be making sure the right rules are in place. As the researchers conclude in their blog post: As a consequence [of this research], we may be able to better understand and control complex multi-agent systems such as the economy, traffic systems, or the ecological health of our planet - all of which depend on our continued cooperation.
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Google's DeepMind pits AI against AI to see if they fight or cooperate - The Verge
Forbes | Growth of AI Means We Need To Retrain Workers... Now Forbes Picture a future where a robot suggests where to go for dinner, which meetings to take or which hotel you should stay at during an important client event. That's just an example of the impact artificial intelligence (AI) can have on the ways we work ... |
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Growth of AI Means We Need To Retrain Workers... Now - Forbes
AUSTIN, Texas, March 4, 2020 /PRNewswire/ --CB Insights today named SparkCognition to the fourth annual AI 100 ranking, showcasing the 100 most promising private artificial intelligence companies in the world. This makes SparkCognition one of only two companies to be listed on every AI 100 List since its inauguration in 2017.
"It's been remarkable to see the success of the companies named to the Artificial Intelligence 100 over the last four years. The 2019 AI 100 saw 48 companies go on to raise $4.9B of additional financing and nine got acquired," said CB Insights CEO Anand Sanwal. "It has been gratifying to see that CB Insights' data-driven approach to identifying the top AI companies using patents, customer traction, investor quality, market sizing and more has become so effective at picking the AI winners of tomorrow. We look forward to seeing what the 2020 AI 100 companies will accomplish over the course of this year and beyond."
In addition to disrupting core sectors including healthcare, retail, and finance, the 2020 AI 100 companies are revamping the broader enterprise tech stack. These companies span the globe, from the US, UK, China, Chile, and South Africa, and are supported by more than 600 investors.
"We're very happy to be named to CB Insights' AI 100 List for the fourth time," said Amir Husain, Founder and CEO of SparkCognition. "2019 was a banner year with tremendous value delivery to our clients, a $100M funding round, significant product releases, and seminal advancements in our AI research. And we are poised for an even more fantastic 2020!"
Through an evidence-based approach, the CB Insights research team selected the AI 100 from nearly 5,000 companies based on several factors including patent activity, investor quality, news sentiment analysis, proprietary Mosaic scores, market potential, partnerships, competitive landscape, team strength, and tech novelty. The Mosaic Score, based on CB Insights' algorithm, measures the overall health and growth potential of private companies to help predict a company's momentum.
SparkCognition is a leading industrial artificial intelligence company that builds AI solutions for industrial applications, working with industries including energy, aerospace and aviation, cybersecurity, and more. With a foundation of deep AI expertise and investment in research and advancing the science of artificial intelligence, SparkCognition currently offers four main products: SparkPredict, an analytics solution, Darwin, a data science automation platform, DeepArmor, a cybersecurity platform, and DeepNLP, a natural language processing solution. In October 2019, SparkCognition announced the close of its $100M Series C funding round from investors including March Capital Partners, Temasek, Kerogen Digital Solutions, and Hearst Ventures.
Quick facts on the 2020 AI 100:
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About SparkCognition:
With award-winning machine learning technology, a multinational footprint, and expert teams focused on defense, IIoT, and finance, SparkCognition builds artificial intelligence systems to advance the most important interests of society. Our customers are trusted with protecting and advancing lives, infrastructure, and financial systems across the globe. They turn to SparkCognition to help them analyze complex data, empower decision-making, and transform human and industrial productivity. SparkCognition offers four main products:DarwinTM, DeepArmor, SparkPredict, and DeepNLPTM. With our leading-edge artificial intelligence platforms, our clients can adapt to a rapidly changing digital landscape and accelerate their business strategies. Learn more about SparkCognition's AI applications and why we've been featured in CNBC's 2017 Disruptor 50, and recognized three years in a row on CB Insights AI 100, by visiting http://www.sparkcognition.com.
Contact:Cara SchwartzkopfSparkCognitioncschwartzkopf@sparkcognition.com512-956-5491
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In BriefScientists have created an artificially intelligent systemthat is capable of producing cutting edge paintings that someconsider to be better than works created by humans. How do thepaintings, and other AI creations, relate to seminal criticisms ofmodern art? An AI Picasso
Scientists are using artificial intelligence (AI)to find a new system for generating art and testing their results on the public. The system, called a generative adversarial network (GAN), works by pairing two AI neural networks:a generator, which produces images, and a discriminator, which judges the paintings. It does this based on the 81,500 example paintings and knowledge of different artistic styles (such as Baroque, Impressionism, and Modernism) it was taught. The suggester creates an image, the discriminator criticizes it, and the conversation leads to a work of art.
The scientists changed the way that AI usually produces art by having the generatoronly create works that did not fall into a preexistent category of painting they did this by maximizing deviation from established styles and minimizing deviation from art distribution, according to the abstract.
Mark Riedl, an associate professor at the Georgia Institute of Technology in Atlanta,said that he liked the idea that people are starting to push GANs out of their comfort zone this is the first paper Ive seen that does that.
After the paintings were produced, the scientists conducted a survey with members of the public in which they mixed the AI works with paintings produced by human artists. They found that the public preferred the works by AI, and thought they were more novel, complex, and inspiring.
Paul Valry, who Walter Benjamin used as a starting point for his essay The Work of Art in the Age of Mechanical Reproduction, wrote in 1931: We must expect great innovations to transform the entire technique of the arts, thereby affecting artistic invention itself and perhaps even bringing about an amazing change in our very notion of art.
He was referring to the modernist period, in which new techniques and ideologies changed the way art was perceived. We may be experiencing a similar upheaval in the art world. Benjamins criticism of the exact copies that could be produced by the second half of the 20th century centered around the idea that even the most perfect reproduction of a work of art is lacking in one element: its presence in time and space, its unique existence at the place where it happens to be.
This AI project possesses this property. It does not just copy or manipulate, as Google Deep Dream does, but is able to producetrue works of art by being actively programmed to be novel and creating originals in a specific place. These pieces are more similar to Aiva, an AI composerthat also could not be detected by humans, than it is to Deep Dream.
We are entering an age where AI is becoming increasingly ubiquitous and competent in almost every fieldElon Musk thinks it will exceed humans at everything in by 2030 but art has been viewed as a pantheon of humanity, something quintessentially human that an AI could never replicate.
Studies such as this show that our artistic leanings may not be off limits and with AI conquering humans at our own games, like chess how long is it before we create a Picasso program that is superior to any current human artist and immortal to boot?
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AI Can Now Produce Better Art Than Humans. Here's How. - Futurism
Advances in artificial intelligence and deep learning have changed our lives. We are already using it even without realizing it: AI helps to power Googles search engine, Teslas self-driving cars, Apples voice assistant, and Amazons shopping recommendations.
The impact of artificial intelligence in retail and ecommerce is also growing. While ecommerce giants like Amazon, Walmart, and eBay have used these capabilities behind the scenes for years, ecommerce entrepreneurs can now also do the same. Algorithmic technology and AI can be incredibly helpful tools to grow sales and optimize various aspects of ecommerce operation, from pricing to demand planning.
Here are the three most crucial applications for this tech.
Todays online retail industry is rapidly changing and presenting new challenges to ecommerce startups. The markets have become increasingly competitive, to the point where the price of each individual product must change frequently in response to market dynamics. Therefore, even for an online merchant with a couple hundred SKUs, continuous adjustment of prices quickly becomes a challenge.
Repricing merchandise strategically is particularly crucial on Amazon, where sellers constantly compete to land the Amazon Buy Box a coveted spot that essentially guarantees its winner vast sales. To select products for placement, Amazon uses sophisticated algorithms to assess merchants performance metrics such as ratings, reviews, shipping, pricing, and quality of service. For these reasons, optimal pricing of merchandise on Amazon requires sellers to go much deeper than guesstimates.
AI solves this problem by repricing merchandise using complex learning algorithms that continuously assess the market dynamics and changes in competitive environment.
Managing inventory availability across channels is one of the biggest worries for ecommerce businesses. Being out of stock is a nightmare scenario, as it takes days to replenish products and can heavily affect merchants revenues. On the other hand, overstocking increases business risks and capital requirements.
The problem with forecasting inventory velocity in a rapidly changing market is that both demand and competition change quite frequently. In such markets, a hindsight perspective traditionally implemented with the help of BI technology is no longer sufficient. In order to reach operational efficiency, retailers must employ accurate demand forecasting and predictive analytics.
Artificial intelligence and learning algorithms can help with order velocity forecasting. They can identify key factors that affect the velocity of orders, and monitor the factors impact to accurately model velocity and inventory requirements. The beauty of learning systems is that they get smarter over time, enabling merchants to accurately predict their inventory needs.
The other key aspect of retailer operation is managing the assortment of products that is, which products to keep selling, which products to add, and which products to discontinue. Like inventory planning, assortment planning requires a good amount of forecasting. Merchants need to monitor market trends and changes in demand to understand the competitiveness of products.
Although a person can analyze the past performance of products and categories, accurate forecasting requires a sophisticated algorithmic model. It must assess the relationships across products, influences of various events, and impact of competition and pricing.
Giants like Amazon and Walmart constantly monitor their product assortment and have a team of data scientists dedicated to this task. For the first time, these advanced capabilities are now available to ecommerce startups, thanks to advances in AI and algorithmic technology.
The beauty of online commerce is that it is completely digitalized. All the data from the operations, the market, and the competition can be consolidated and analyzed. It can be examined historically and now, with the help of AI technology, forecasted as well.
Now is the time for ecommerce businesses to get smarter and reach operational excellence. Logistics used to be the core competency of retail; today, algorithms constantly crunch data, predict market trends, and respond to market changes in real time. Such advancements are only possible because of AI.
Victor Rosenman is the founder and CEO of Feedvisor, an Algo-Commerce company that helps online retailers make business-critical decisions in real time.
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PHILADELPHIA, July 9, 2020 /PRNewswire/ -- Infervision is pleased to announce the U.S. Food and Drug Administration (FDA) 510(K) clearance of the InferRead Lung CT.AI product, which uses the state-of-the-art artificial intelligence and deep learning technology to automatically perform lung segmentation, along with accurately identifying and labeling nodules of different types. InferRead Lung CT.AI is designed to support concurrent reading and can aid radiologists in pulmonary nodule detection during the review of chest CT scans, increasing accuracy and efficiency. With five years of international clinical use, Infervision's InferRead Lung CT.AI application is a robust and powerful tool to assist the radiologist.
InferRead Lung CT.AI is currently in use at over 380 hospitals and imaging centers globally. More than 55,000 cases daily are being processed by the system and over 19 million patients have already benefited from this advanced AI technology. "Fast, workflow friendly, and accurate are the three key areas we have emphasized during product development. We're very excited to be able to make our InferRead Lung CT.AI solution available to the North American market. Our clients tell us it has great potential to help provide improved outcomes for providers and patients alike," said Matt Deng, Ph.D., Director of Infervision North America. The Company offers the system under a number of pricing models to make it easy to acquire.
The company predicts the system may also be of great benefit to lung cancer screening (LCS) programs across the nation. Lung cancer is the second most common cancer in both men and women in the U.S. Survival rates are 60% in five years if discovered at an early stage. However, the survival rate is lower than 10% if the disease progresses to later stages without timely follow-up and treatment. The Lung Cancer Screening program has been designed to encourage the early diagnosis and treatment of the high-risk population meeting certain criteria. The screening process involves Low-dose CT (LDCT) scans to determine any presence of lung nodules or early-stage lung disease. However small nodules can be very difficult to detect and missed diagnoses are not uncommon.
"The tremendous potential for lung cancer screening to reduce mortality in the U.S. is very much unrealized due to a combination of reasons. Based on our experience reviewing the algorithm for the past several months and my observations of its extensive use and testing internationally, I believe that Infervision's InferRead Lung CT.AI application can serve as a robust lung nodule "spell-checker" with the potential to improve diagnostic accuracy, reduce reading times, and integrate with the image review workflow," said Eliot Siegel, M.D., Professor and Vice Chair of research information systems in radiology at the University of Maryland School of Medicine.
InferRead Lung CT.AI is now FDA cleared, and has also received the CE mark in Europe. "This is the first FDA clearance for our deep-learning-based chest CT algorithm and it will lead the way to better integration of advanced A.I. solutions to help the healthcare clinical workflow in the region," according to Matt Deng. "This marks a great start in the North American market, and we are expecting to provide more high-performance AI tools in the near future."
About Infervision Infervision is committed to the clinical application of artificial intelligence and deep learning technologies in health care, providing AI-based platforms and tools fully integrated with medical workflows. Health providers in over 10 countries in Asia, Europe, North America use Infervision's highly precise and efficient clinical tools, empowering them with improved clinical insights. Infervision currently has 8 global offices and over 300 employees worldwide. Each day Infervision helps process over 55,000 exams, and accumulatively 19M case reviews since 2015. Learn more about Infervision's product suites at global.infervision.com
Reference for LC survival rate: https://seer.cancer.gov/csr/1975_2017/browse_csr.php?sectionSEL=15&pageSEL=sect_15_table.12
Contact:
Haiyun WangMarketing Managerwhaiyun@infervision.com+1 765-637-8892
View original content:http://www.prnewswire.com/news-releases/infervision-receives-fda-clearance-for-the-inferread-lung-ctai-product-301091145.html
SOURCE Infervision North America
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Infervision Receives FDA Clearance for the InferRead Lung CT.AI Product - BioSpace
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Adobe and Red Hat, an IBM company, have announced a strategic partnership to that includes cloud hosting under Red Hat, Adobe joining IBM's partner ecosystem and IBM extending services across Adobe's core enterprise applications. Company officials say the partnership will strengthen real-time data security for enterprises, focusing on regulated industries, and enable companies to deliver more personalized experiences across the customer journey.
The partnership will initially focus on:
As part of the partnership, IBM has named Adobe its "Global Partner for Experience" and will begin adopting Adobe Experience Cloud and its enterprise applications.
IBM once competed with Adobe in customer experience and marketing software offerings. But it sold off the former to HCL in 2018 and then six months later dumped its marketing software to a private-equity investor.
In other customer experience and marketing software news ...
Microsoft has released Microsoft Dynamics 365 Customer Voicein a move to help organizations collect and distribute feedback from customers across teams. Company officials said the software comes in the form of ready-to-use templates with default questions designed to fit feedback scenarios: periodic customer feedback, post-delivery, post-visit and post-support call. Users can customize questions in the templates to fit specific product or service needs.
Dynamics 365 Customer Voice includes built-in integrations with all Microsoft business applications including Microsoft Dynamics 365, Microsoft Power Apps and integrations to external third-party applications through Microsoft Power Automate.
Users can send a survey to customers while using integrated data from other applications, such as open rates, marketing channel preferences and product and service preferences. Survey results integrate back into business applications and are available to anyone who engages with the customer. This information can then be used to enhance customer profiles in Microsoft Dynamics 365 Customer Insights.
Users can also define their customer satisfaction (CSAT) metric and map that score to a new survey to use that metric to collect feedback periodically and track trends over time on the dashboard. Users can also set alerts on metrics to notify relevant business users when they receive poor feedback.
Google has made Recommendations AI, which is designed to help organizations drive personalized product recommendations to their customers, publicly availableto all customers in beta. Recommendations AI helps piece together the history of a customers shopping journey and serve them with personalized product recommendations. Recommendations AI also handles recommendations in scenarios with long-tail products and cold-start users and items.
It uses deep learning models and user metadata to draw insights across millions of items at scale and iterate on those insights. Recommendations AI also delivers model management experience in a managed service. Users can start using the product by creating a Google Cloud project and integrating and backfilling catalog and user events data with tools such as Merchant Center, Google Tag Manager, Google Analytics 360, Cloud Storage and BigQuery.
Qubit, which offers AI-led merchandising and personalized experiences, announced it has furthered its partnership with Google Cloud, making Recommendations AI available for ecommerce teams. The product recommendations solution is integrated within Qubits new product interface for merchandising.
Terminus, a provider of Account-Based Marketing (ABM) software, has announced its July product release and Terminus Engagement Hub. Its offerings Advertising Experiences, Email Experiences, Web Experiences and Chat Experiences are all now available in The Terminus Engagement Hub. Terminus Chat Experiences are now fully integrated into the Terminus Engagement Hub.
Users can see their target accounts and segments flow automatically into Chat Experiences; personal greetings and chats for visitors are automatically routed to the appropriate outbound rep on mobile or desktop. Chat Views is designed to helps sales and marketing teams identify qualified inbound leads and real-time sales conversations on websites with target accounts.
Salesforce has announced new Marketing Cloud innovations, including integrations with the acquired Evergage technology. New Interaction Studio innovations leverage Einstein and technology integrated from Evergage. They include:
The enterprise edition of the Salesforce Pardot marketing automation now includes new features to support complex B2B marketing teams:
Salesforce also announced a Datorama integration with Tableau to help marketers optimize their budget and data with features that include:
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Adobe Partners With Red Hat, Google Updates AI Offering and More CX News - CMSWire