AI Regulation, our take.

Last week, EU has published a proposal for an AI regulation, outlining risk classification for various AI applications, and proposed measures to be taken for each risk category. This came as a follow-up on EU guidelines for trustworthy AI suggesting stricter policies. Discussions in the media have been extensive since then, we at MainlyAI have been monitoring them with curiosity and excitement, and now it's time for our 5 cents.

AI has been proven to be an efficient technique in many industries. And as with many other technologies, it is important to regulate its use to guarantee the trustworthiness. When you download software to your computer or phone, you want to be sure that the software does not cause unwanted behaviors. This is even more important when we talk about safety-critical industries such as the automotive industry or medtech, because one must be able to rely on the software. Therefore, there are certifications for software at different levels. And, in a similar way, one should be able to know that AI algorithms do not add unwanted behaviors in the contexts in which they are placed. If a model has been trained on incorrect data, the algorithm will provide incorrect decision support and such situations can be avoided by regulating the use of AI at different levels.
 
One of the advantages of the proposal is that by regulating the introduction of AI in industries, one can, strange as it may sound, promote innovation. Without regulation, high-risk industries cannot rely on third-party developers. If you have a way of certifying an algorithm and guaranteeing that it does not cause unwanted behaviors, you can be safe when using it. Another advantage is that the algorithms around us will be reliable, and society will have greater acceptance of AI. 

The regulation may, however, lead to lower efficiency of the algorithms and somewhat slower market introduction for the AI ​​solutions in Europe. But no matter how much we love AI and efficiency in reaching the desired KPIs, we do think that trustworthiness in all its forms - safety, privacy, security, transparency, explainability, non-bias, and non-discrimination - is of the highest importance and must be treated as a hygiene factor. Let's embrace it.

MainlyAI in Production and Logistics project EXPLAIN

MainlyAI has, together with AstraZeneca, Hitachi-ABB, KTH, RISE IVF, Scania-CV, SECO Tools, and Uppsala University, been granted funding by the Swedish governmental funding agency, VINNOVA, in the Production 2030 call. The project, named Explainable and Learning production & logistics by Artificial Intelligence (EXPLAIN) will run over three years with a total budget of 13 MSEK.

The aim of the EXPLAIN project is to increase the profitability, sustainability, and competitiveness of the Swedish manufacturing industry by an innovative combination of virtual production technologies and AI algorithms. EXPLAIN will explore this combination as a unique way to provide increased access to new knowledge and skills in the production area, which is not possible with the current industrial practice of applying virtual production technologies alone.

The EXPLAIN project is coordinated by Uppsala University. The application was allocated full funding amount by VINNOVA in strong competition, with the success rate of less than 20%. A complete list of funded projects can be found at https://produktion2030.se/grattis-till-atta-nya-projekt-2021/ (in Swedish).

About MainlyAI
MainlyAI AB (559258-7538) is a research and technology-based company with the objective to allow for businesses to share data and insights in a safe and privacy preserving way and hence speeding up and democratizing the introduction of AI technologies. The approach of MainlyAI is centered around a platform as a service with an API providing a knowledge database of data and insights, and services simplifying the data/insight access and adoption of AI technologies for business.

For more information email contact@mainly.ai.

MainlyAI awarded VINNOVA funding in the Innovative Startup program

Today, MainlyAI AB was awarded funding by the Swedish Governmental Innovation Agency VINNOVA. In the project, MainlyAI will continue to develop its AI-solutions for safely sharing machine readable knowledge between businesses.

With this funding, MainlyAI will be able to take important and necessary steps towards market introduction of our products and services for creating value from compound machine-readable knowledge from businesses and hence increasing the benefits of AI for our customers.

The project will run during 2021 with possible extension in 2022 and onwards.

About MainlyAI
MainlyAI is a research and technology-based company with the objective to allow for businesses to share data and insights in a safe and privacy preserving way and hence speeding up and democratizing the introduction of AI technologies. The approach of MainlyAI is centered around a platform as a service with an API providing a knowledge database of data and insights, and services simplifying the data/insight access and adoption of AI technologies for business.

For more information email contact@mainly.ai.

MainlyAI continues to strengthen its development team: meet Kristofer Älvring

1.    What are you looking forward to the most when starting at MainlyAI?

            If you’re going to build algorithms, which will change the world, you need a swift, smart and fierce crew skilled in navigating stormy seas, capturing whales and dodging sharks, all whilst having great fun at the same time. I feel really lucky to be part of this adventure! Also, the loot is great!

2.    What do you think about the pace of AI deployment in the industry and why do you think it’s slow? 😉

            When the time comes to invest in your company’s growth, you can choose between spending money on hiring a new domain expert or buying specialized technology to augment the existing workforce. Machine learning and AI promise a technology, which will learn from its environment and its past actions. Building this learning technology is just the first step though. Without training, the AI is neither a domain expert nor specialized enough to be of help to said experts. It will get there eventually. However, some patience and supervision are required.

This is where it becomes tricky. Are you guaranteed to get a decent return on your investment? What if it doesn’t learn well enough? It takes a leap of faith and patience. Most of us, when hungry, prefer to buy the bread directly from the bakery, rather than planting seeds and hoping for a rich crop next year. A hungry mind-set doesn’t plan for the future and will spend all time gathering the lowest hanging fruit forever.

In time, when it becomes more common to see machine learning grow new and better domain experts from humble beginning, the thought of AI investments as a sustainable choice will come naturally. I’m here to help those thoughts along.

3.    Where do you see companies could benefit from AI in their work to set and achieve sustainability targets?

            There are plenty of areas where we simply can’t write a code that would do the job. As an example, machine learning enables computer vision classification in ways that were virtually unthinkable only a few years ago. You can’t program a car to drive in traffic. However, you can write a machine learning algorithm, which learns from its environment, so that it eventually can drive that car. The world if full of complex dynamics, where not even the best of domain experts can figure out the solutions, which connect our reality. Often when you want to reach holistic goals like sustainability targets, those exact complex dynamics must be sorted out and controlled. That provides an excellent opportunity for employing AI solutions which can learn to see new connections in the data that humans wouldn’t be able to discover otherwise.

4. Companies are not operating in vacuum; many processes are similar. What would be the natural next step in development of AI?

            We send our children to school to learn from each other and from past experiences. Lots of work is being done in creating such schools for AI-algorithms, where we can share data and learn from each other in a never ending information brokering. I think these new “botnets” are fascinating constructs which could help mitigate the cost and risk of training new algorithms on your own.

5. Five quick questions with Kristofer:

  1. XBOX or PS?
    • If I play a game I use a real rig and not some puny console.
  2. Fortran or Python?
    • Python!
  3. Spotify or Vinyl?
    • Spotify
  4. Quest or First-Person Shooter?
    • Quest
  5. Tea or coffee?
    • Coffee

6. Tell us a fun fact about yourself

  1. I’ve been playing roleplaying games as a hobby since I was 10.
  2. I think Mark Lawrence is the best author in the world and I simply can’t be bothered by anything less.
  3. I think the future should be more like Pokemon rather than Cyberpunk.
  4. I wrote the first botnet ever using an IRC-server and C code.
  5. I also wrote first ever Swedish weather reporting public web application.
  6. I once recommended the use of Beagle SQL over MySQL 2.0, because the future of MySQL seemed pale.

Diamyd Medical invests in MainlyAI

Diamyd Medical’s investment will give a 20% ownership and a board seat in MainlyAI. The investment will facilitate MainlyAI’s strategic focus on applying artificial intelligence, where a first project is sustainable production within the pharmaceutical sector.

As announced in  December 2020, Diamyd Medical and MainlyAI are, together with the Royal Institute of Technology (KTH), engaged in a VINNOVA funded project to design, test and build a sustainability framework powered by artificial intelligence for Diamyd Medical’s production facility in Umeå, Sweden.

Ulf Hannelius, CEO of Diamyd Medical will, following the investment, join MainlyAI’s Board of Directors.

About MainlyAI
MainlyAI is a research and technology-based company focused on helping businessess to become more sustainable using artificial intelligence. The company enables sharing of data and insights between enterprises in a safe and privacy preserving way, hence speeding up and democratising the introduction of AI technologies. The approach of MainlyAI is centered around a platform-as-a-service based on state-of-the-art artificial intelligence technologies providing decision support, trend analysis, automation and services simplifying the adoption of AI technologies for business and research.

About Diamyd Medical
Diamyd Medical develops therapies for type 1 diabetes. The diabetes vaccine Diamyd® is an antigen-specific immunotherapy for the preservation of endogenous insulin production. Significant results have been shown in a genetically predefined patient group in a large-scale metaanalysis as well as in the Company’s European Phase IIb trial DIAGNODE-2, where the diabetes vaccine was administered directly into a lymph node in children and young adults with recently diagnosed type 1 diabetes. A new facility for vaccine manufacturing is being set up in Umeå for the manufacture of recombinant GAD65, the active ingredient in the therapeutic diabetes vaccine Diamyd®. Diamyd Medical also develops the GABA-based investigational drug Remygen® as a therapy for regeneration of endogenous insulin production and to improve hormonal response to hypoglycaemia. An investigator-initiated Remygen® trial in patients living with type 1 diabetes for more than five years is ongoing at Uppsala University Hospital. Diamyd Medical is one of the major shareholders in the stem cell company NextCell Pharma AB.

Diamyd Medical’s B-share is traded on Nasdaq First North Growth Market under the ticker DMYD B.

ALISTAIR is Kicked-Off!

Rolling rolling rolling!

ALISTAIR has been formally kicked-off in January with the representatives from all partners. Work package drivers are in place, project spaces are in place, and two initial work packages have been initiated. System architecture is being put together, system requirements are being collected, and inventories of sensors and actuators available on the market, with a specific focus on sensor for clean rooms are being created.

MainlyAI awarded VINNOVA funding for AI-driven sustainable production

MainlyAI AB together with Diamyd Medical and KTH Royal Institute of Technology have been awarded funding by the Swedish Governmental Innovation Agency VINNOVA for a project that will design, test and build a sustainability framework powered by artificial intelligence (AI) for Diamyd Medical’s production facility in Umeå, Sweden.

The project ALISTAIR (Artificial Intelligence for Sustainable Production), with a total funding of 13 MSEK including in-kind contribution, is part of the VINNOVA program “AI in service of the climate”. The project will study how AI technologies can be applied to (i) reduce greenhouse gas emissions of production plants, and (ii) support decision-making and trade-offs when controlling production plants with regards to vital parameters such as production speed, employee wellbeing, sustainability goals and waste management. The ultimate goal of the project is to present techniques and strategies general enough to be applied and scaled up in production facilities across industries.

Within the scope of the ALISTAIR project, there is a unique opportunity to design, implement, and evaluate the project results to the brand-new drug production facility being setup by Diamyd Medical in Umeå, the Capital of Västerbotten County in Sweden. The new plant will as a first priority produce recombinant GAD65, the active pharmaceutical ingredient in the therapeutic diabetes vaccine Diamyd® currently in late-stage clinical development. The 10,000 square feet site, comprising of clean rooms, laboratory facilities and office space, will facilitate full control, predictability and scalability of the production technology of the active ingredient.

“We are very glad to have this opportunity to work with recognized experts within the field of both AI and sustainable production”, says Ulf Hannelius, CEO of Diamyd Medical. “This project will directly support both the development of our production facility as well as enable data driven decision making and sustainability thinking in our operational work as we grow as a company.”

“We look forward to apply modern AI techniques and to further develop our AI solutions in the service of the climate to minimize the greenhouse gas emission of Diamyd Medical’s new production plant in Umeå”, says Elena Fersman, Adjunct Professor at KTH and Chairman of MainlyAI.

“This is a fantastic project focusing on designing a new sustainable and circular production plant in Umeå already from the start with the use of digitalization and AI as the enabler, says Monica Bellgran, Professor in Production Management, Director of KTH Research Platform ‘Industrial Transformation’. ”It is a quite unique opportunity we don’t see that often in Sweden, and from KTH we are delighted to be part of the consortium together with Diamyd Medical and MainlyAI. Thanks to the funding from VINNOVA we believe that Diamyd Medical’s production facility can be a great showcase demonstrating how AI contributes to sustainable production”.

About MainlyAI
MainlyAI is a research and technology-based company with the objective to allow for businesses to share data and insights in a safe and privacy preserving way and hence speeding up and democratizing the introduction of AI technologies. The approach of MainlyAI is centered around a platform as a service with an API providing a knowledge database of data and insights, and services simplifying the data/insight access and adoption of AI technologies for business.

About KTH Royal Institute of Technology’s participation in the project
Researchers from two departments at KTH; Machine Design (lead by Professor Martin Törngren), and Sustainable Production Development (Lead by Professor Monica Bellgran), will participate in the new research project.  

About Diamyd Medical
Diamyd Medical develops therapies for type 1 diabetes. The diabetes vaccine Diamyd® is an antigen-specific immunotherapy for the preservation of endogenous insulin production. Significant results have been shown in a genetically predefined patient group in a large-scale meta-study as well as in the Company’s European Phase IIb trial DIAGNODE-2, where the diabetes vaccine is administered directly into a lymph node in children and young adults with newly diagnosed type 1 diabetes. A new facility for vaccine manufacturing is being set up in Umeå for the manufacture of recombinant GAD65, the active ingredient in the therapeutic diabetes vaccine Diamyd®. Diamyd Medical also develops the GABA-based investigational drug Remygen® as a therapy for regeneration of endogenous insulin production and to improve hormonal response to hypoglycaemia. An investigator-initiated Remygen® trial in patients living with type 1 diabetes for more than five years is ongoing at Uppsala University Hospital. Diamyd Medical is one of the major shareholders in the stem cell company NextCell Pharma AB.

The price is right (?) Or how to monetise on your fantastic AI product?

So… you are in the process of creating an AI product that will become a smashing success? Congratulations! Many exciting, frustrating, long hours of development lie ahead.

The great news is that the recent boom in companies adopting and incorporating AI technologies into their daily processes on a wider scale has created an almost insatiable demand for various AI solutions. And one of the greatest advantages of a product company is its ability to create predictable recurring revenues that will drive the value of the company, especially, in the low interest rate environment where future profits do not to be too heavily discounted.

What pricing models should you adopt to be successful in the medium run and ensure that your product flies off the shelves? Unfortunately, there is no fit-all answer. The choice of a correct model is not always a given.

Consider a couple of options on how to charge your clients for your (give yourself a pat on the shoulder) great product – a fixed and a floating fee. Each comes with its pros and cons.

A fixed monthly fee could be a great option. The upside of a fixed fee is that it will allow for better budgeting for both you and your client. On the downside, a fixed monthly fee might not suit all clients. They could be reluctant to pay fixed fees, especially if they foresee a varying degree of usage of the product.

A varying fee could be a solution to the latter. Such a fee can come in many shapes and forms. Here are a few that could be worth mentioning:

  • Outcome-based – agree with your client on the final deliverables in advance and charge once those are successfully delivered.
  • Revenue share – imagine if you could share the extra revenue or profit that your product is creating for the client. The expression “We are in the same boat” would take on a completely different meaning.
  • Per insight – at the end of the day, this is what an AI product creates most of the times, insights. Why not charge for those?
  • Per data point – all algorithms need data to educate themselves. The more data they consume, the smarter they become. Hence adding value to the customer. Most people are willing to spend money on educating their young, right?

Agreeably, these might not be easy to construct and quantify in a fair way. We are not going to go into an in-depth discussion of the above-mentioned models today. The list is not complete and could go on. Instead, we’ll leave you with another question: “What if the clients could trade their data and insights with each other in a safe, privacy preserving way?” Data in the new “Industry 4.0” world is a commodity, as oil was under the “old” economic order. Any commodity can be traded…

Organisational Management for AI – Five Key Principles

Artificial Intelligence is a science that mimics human intelligence and other phenomena that exist in nature, such as evolution. Plenty of concepts that work out for humans are therefore directly applicable to algorithms. Concepts that work out for organisations of humans are relevant for organisations of algorithms. Therefore, when recruiting your team of AI workers, think carefully how you want to organise them and how they should complement each other. Below we describe five key principles to consider when building a team of AI brains.

1. Decide who is in charge

We won’t go into a never-ending discussion of centralised versus decentralised control. One thing is however clear: things tend to fall between the chairs when there is no clear responsible person AI.

2. Make sure the team members complement each other

We all know about the benefits of diversity and inclusion in teams of people. Different opinions and approaches are great in brainstorming sessions and the same goes for AI brains. Just like people, different algorithms complement each other and find better solutions faster.

3. Make sure the team members don’t have communication problems

When several brains work together, they better make sure to have access to the latest information. When a colleague sees a piece of new data, finds out a piece of new information, or comes to a new conclusion, it needs to be communicated instantly to other colleagues working on the same problem. In the AI world we call it common state space, and a mechanism of tackling data updates is linked data. In addition, communication problem can arise when AI brains do not talk the same language (can be solved through adaptors) or do not have the same background (can be solved through semantic mapping of concepts).

4. Healthy competition never hurts

Let two AI colleagues compete in solving the same problem. It will consume some extra resources but will bring multiple benefits: redundancy, opportunity of doing federated learning, and finding out what algorithm gives you the best results.

5. Know your heroes

Rewards are important, also in the world of AI brains. It’s the main thing behind reinforcement learning, while training your algorithm, but also among different algorithms – keep track of your best machine learning models for each specific purpose, so that you know whom to turn to in the future.

Six Advantages of Digital Twins

A digital twin is a digital representation of something. Often, it is a digital representation of a physical object, but in more general sense it can represent a complex system that may consist of a combination of hardware, software, humans and environment. Such as a production process, for example. Or an industrial robot, a cat, a human. Or just air. Anything that is of interest of keeping track of, predict changes, optimize and play around with. To create a digital twin of something physical, we make use of sensors and actuators to tap into the data and control capabilities. Or, if it’s a binary twin of your smile with the only mission of tracking it, we can make use of cameras, serotonin level in your body or just ask the twin of your teeth if they can see the light. In this blog we will talk about the reasons for having a digital twin and what to use it for.

1. Always reflecting the current state

Checking up on your car or a fleet of cars, production plant, wind turbine, wine yard or mining facility may not always be easy because of the complex mechanics, physically distributed things and hard-to-access locations. In addition, regular health-checks are not always good enough when you need to be on top of things as they happen, and to be able to prevent anything unwanted happening. Observability is a pre-requisite for a successful data-driven management of whatever you want to manage.

2. Useful for what-if scenarios

First there were models and simulators. Then they evolved to twins. One can do a lot with a model but often it’s static and needs to be adjusted from time to time to reflect the reality. Twins evolve together with the reality in a data-driven fashion. Communication with the twin is often implemented to be bi-directional meaning that not only the reality makes an effect on the twin, but also changes in the twin effect the reality, like a voodoo doll. And as much as we all love experiments we normally do them in experimental environments and not in live systems. The fine property of a digital twin is that at any moment one can take a snapshot of the latest state and save it as a model to run experiments on. And the classical type of experimentation is what-if scenarios. What if I change an ingredient in my production process? What if I de-centralise my organisation? What if I replace a supplier? What would it imply, both in a short- and long-run?

3. Can be used for simulations

As in the previous paragraph, taking a snapshot of your twin gets you a perfect latest model to experiment with. On can also run simulations, fast-forwarding the development of things along the way. Imagine you have a model of a city that you let evolve by itself at a high pace. Will the city double its size in 20 years? What would the pollution levels be? What the would the GDP be? Almost like SimCity (for those of you who remembers) but based on a latest snapshot of a real city.

4. Can be used for property checks and decision support

When working with digital twins we are in an open world assumption. As soon as we have taken a snapshot of a twin and created a model of environment we are in a closed world assumption, which is an approximation of reality but so much nicer for formal verification community as system properties can be formally checked and guaranteed. One can, for example, check that the level of greenhouse gas emissions in a production plant never exceeds a certain threshold. Of, it it actually does, one can get an explanation of the root-cause and a suggestion of how to act differently.

5. Abstract away the details

The beauty of abstraction is that one can focus on what’s vital for you. This is obvious when doing an abstraction of a piece of software. If your level of abstraction is too high, you may miss some important properties. If it’s too low, then you are not far from the original piece of software, and drowning in its complexity. Similar with systems that are more than just software. If it’s a production plant and you only focus on it’s productivity at any price, you can omit the cost monitoring from your twin. Or, if you don’t bother about contributions to climate change you don’t need to collect that data either. But we believe that you do care about both the cost and climate, so let’s make sure we keep focus on them.

6. Can control the physical twin

As we said before, the relationship with the digital twin is bi-directional, like with a voodoo doll, but with a positive twist to it. If you have branched out a model out of your twin, experimented with different what-if scenarios, simulated 10 years ahead, checked all the vital properties and converged on a necessary change in your system, often you can implement it though the twin by actuation. You can, for example, limit speed of your autonomous trucks to have more positive effect on safety, of decrease the temperature of your production facility to improve your carbon footprint. And, given that you have connected supply chains, you can also tweak ingredients in your production line or even make upgrades to your hardware. Don’t experiment on your workforce though – there we still recommend human touch.