We recently had the pleasure of seeing the newly opened Microsoft Quantum Materials Lab in Kgs. Lyngby Denmark. Considering that quantum computing has been popping up in the media for almost 25 years, what is it that is causing it all of a sudden to create such hype? One reason is that big companies like IBM, Intel, Microsoft and Google have started investing in it at an unprecedented scale. The reason for them to do so is obvious, quantum computing promises almost infinite amounts of computing power. Like nuclear fusion, it can truly change how the world works. A Microsoft representative mentioned that they needed several data-centers just to simulate a machine with only, say, 50 qubits. Qubits being the equivalent of bits in the quantum computing world. Amongst other things even such a small machine promises instant brute-force decryption of even the toughest cryptographic codes.
To truly understand how this can impact AI you just need to scroll back a few years. What really kicked off the modern AI revolution was that during the naughties (2000’s) really good software was built to train AI’s on GPU’s. This made it all of a sudden feasible to train on large datasets. If we now have a quantum AI computer then we would likely be able to instantly train on datasets which today take days or even weeks. This means we can also start training on much tougher problems and get much better solutions for existing datasets.
So are we still 25 years away from a quantum AI computer we can start using? The answer is No! Quantum computing research is actually not just one thing. A lot of the research is for a general purpose quantum computer. However hybrid systems with many qubits already exist today that can solve more specific problems. For example, the company D-wave have created a quantum annealing machine with staggering 2000 qubits that you can buy today if your pockets are deep enough. Quantum annealing helps solve optimization problems via a popular method called Simulated Annealing. Most people working with machine learning will have run into this method during their studies, without going into the details, it is a good algorithm for solving a number of machine learning problems. This is also where it starts getting interesting for AI usage. Today the interfaces are non-existent, but people at Google AI and other big co’s are working on bridging the gap between such machines and everyday AI tools such as tensorflow. Within a few years expect that it will be possible to upload your tensorflow code to a quantum cloud service and have it train almost instantly. In the start it will only be for specific types of AI problems but in the future it will solve a whole range of tasks today performed by GPU’s. IBM already has a simple quantum computing cloud service, since it works so fast, it is not rented by the hour but instead by the minute!
There is tremendous interest in how AI is changing industrial vision. Our track on big data and artificial intelligence for industrial vision was fully booked at the High Tech Summit in Copenhagen, Denmark. AI is leading to a paradigm shift in how quality monitoring and optimization is done today.
Many industrial vision companies still rely on software developers for their computer vision algorithms but they are slowly learning that AI can do the same things faster and more robust. Many applications within food and pharma have been too hard to solve with traditional computer vision because of large product variations. Now it is however possible to solve them just by training from examples. This is also giving rise to new applications – we demonstrated our work together with Teknologisk Institut in slaughterhouse cobots that can adapt to and cut meat based on individual variation in animals (Augmented Cellular Meat Production – ACMP). Although still slower than a human, the ability to parallelize the process in cell-based production will ultimately allow such systems to increase throughput while at the same time increasing redundancy and thus lower risks of down-times.
Applications within industrial vision are no longer restricted to individual machines and can effectively through AI-based digital twins optimize entire production lines, leading to higher quality products and increased yield. By combining production level insights with industrial cameras it is possible to train complete systems that learn what to look for in images based on Key Performance Indicators. We saw massive interest from food and pharma-based applications for optimizing fermentation tanks and other complex bio-related multi-step processes. Understanding causes and effects in such processes is very complex for humans and traditional process models, but much easier for AI-based methods. Also, why stop there? The approach scales easily to supply chains – as long as we have access to the data then you no longer have to worry about modelling individual sensors and processes which previously was a show-stopper for your digitalization strategy.
If there is a question that keeps popping up again and again amongst our clients, it is: what is Artificial Intelligence (AI)? Depending on who you ask you will get a different answer. The reason is simple, AI has its roots from multiple sources, including the scientific community, popular culture, and even geography plays a role. If you ask the scientific community in the USA you might get that AI was invented by the military (DARPA) in the 1950’s. Work in emulating human neurons dates back even further. Either way our modern western understanding of what is AI has been heavily influenced by popular culture such as Hal in 2001 Space Odyssey (60’s), Skynet in Terminator (80’s), and more recently from modern TV series such as A.L.I.E. from The 100 (Netflix). On the other hand cultures in Asia have had a different starting point for defining what is AI. For example in Japan there is a culture/folklore surrounding “Tsukumogami” where inanimate objects may possess souls. Even a rock may be considered to be intelligent. This has led to a different understanding of AI and a much broader acceptance of things as intelligent.
In Western culture we have devised scientific methods such as the famous Turing test which seeks to determine if a system exhibits intelligent behaviour under the idea that if it looks and acts intelligently, then it might as well be intelligent. Are there then any rules on when something can or cannot be an AI? One of the big recent notions has been data-based intelligence, popularized by artificial neural networks where millions of neurons are trained to perform intelligent functions. Earlier work saw the use of layered rule-based systems (subsumption architectures) where even simple interations of rules (E.g. if-then-else) gave rise to complicated interations. Recently, the notion of hiveminds where crowd-sourcing is used for decision-making, ideation, etc can probably also be called AI even though in the end it relies on real human brains.
This gives rise to another question, how intelligent does a system or object need to be to be called an AI? Hiveminds have been shown to provide super-human level intelligence, likewise many deep learning systems show super-human performance in highly specific operations like image recognition. On the other hand most people will quickly point out that modern AI’s like Amazon Alexa and Microsoft Cortana are pretty dumb (most were also built around rule-based systems). The answer is that AI’s probably do not need to be very smart if they attempt to mimic human behaviour.
Moving on to the scientific community, you will see throughout the internet Venn diagrams and Onion diagrams where Artificial Intelligence is tried to be described in terms of being an umbrella for machine learning tools (deep learning, optimization), or in terms of applications (expert systems, robotics, etc) or as a subset of various fields (like psychology, mathematics, etc). Especially the tools part has been a bit difficult and boils down to what should an AI be able to do to be called an AI? Most AI systems today do not learn anything on-the-fly. They are in essence “programmed” once and then can only do that one thing. Experts are then needed to “reprogram” (AKA train) for new tasks). So systems that can more quickly learn and do it on-the-fly are clearly the next step for AI’s. Current research in reinforcement learning and transfer learning are two such areas that seek to alleviate this obvious short-coming.
We hope this helped you get a better understanding of what AI is and inspired you to learn more.
// Sensomind Team
Autumn at Konpuku-ji Zen Buddhist Temple in Kyoto
Autumn is a season of change and renewal, evoking strong emotions about the passage of time and our hopes for the future. It also provides a good opportunity to reflect on the years gone by, to establish new perspectives and explore new opportunities.
At Sensomind, the virtues of critical self-reflection and continuous improvement are central to our vision.
These core values are embodied in our products, using technology that by its very nature is constantly evolving and perpetually examining itself to discover new solutions and achieve better results for our clients.
This year, Sensomind has been busy collaborating with industry leaders and cutting-edge researchers to create a wide range of new applications for deep learning technology.
The results so far have already exceeded expectations and we are therefore excited to share some of these successes with you here on our new website.
Sharing is a keyword for us going forward and it is our goal to make sure you are kept up to date about all the latest developments in artificial intelligence, giving you insider knowledge on how they can be applied to your business.
This will be done here on our blog, on our Twitter and LinkedIn pages, as well as through our monthly newsletter.
We hope you will join us on this exciting new journey and we look forward to engaging with you, sharing ideas, and listening to your feedback.
Co-founder & CEO, Sensomind