Monetarisation of AI

1. Status quo

Adoption of Artificial Intelligence (AI) technologies has been relatively slow so far. This has been changing rapidly in the past two-three years. However, the question of what the main hindrance for AI implementation remains. A couple of areas could be highlighted:

  1. Difficult to measure Return on Investment (ROI);
  2. Long time to market.

Let us analyse each point. AI can be used for making existing processes more efficient or developing new products. However, the below reasoning is valid for either scenario.

  1. Firstly, how could one assess economic returns from investing in AI? One way could be by monitoring company’s KPIs. If those are improved due to implementation of AI then one can measure Return on Investment (ROI) that has gone into implementing AI.

However, one only derives economic value from AI through deploying the models into production and not keeping the work on a purely academic level. This has been confirmed by findings in several reports, such as McKinsey[1] & Deloitte[2], that found that progressive AI practices are rewarded. Companies seeing the biggest increases in earnings from AI were not simply following common practices, such as establishing machine learning operations, MLOps, and IT process automation (AI for IT operations, or AIOps), but also spending more efficiently on AI and taking a greater advantage of new technologies. Secondly, the time to market. Entire departments might need to be set up and developed to identify usable data streams, create, train AI models, etc. On top of that, once a new technology is adopted, there is a dip in KPIs and performance. At times the length and the depth of such a dip impedes adaptation of new technologies.

2. What’s missing for improved KPI optimisations?

A lot has happened in the past years. However, there is, of course, substantial room for improvement in the economic returns from investments in AI. This can be achieved via:

  • Improvement of accuracy of AI models
  • Wider availability of models

2.1 How can improvement in KPI optimisation be achieved?

Introducing MIRANDA, which allows to exchange insights in a privacy preserving fashion to improve accuracy of models.

The number of AI models developed world-wide is, probably, approaching the infinity at this stage. This is no news. What is the crazy part in this? Many great initiatives get shelved or are kept at a purely academic level and don’t get a chance to reveal their full potential.

This is painful and frustrating for any company, especially for smaller ones with limited resources that need to adopt latest technologies as a core part of their business: medical companies looking to develop new and personalised treatments, fintechs looking to keep and expand their customer base through minimising customer churn… The list could go on, but you get the point.

To help them overcome these challenges, MainlyAI set out to build a platform that enables companies across industries to share insights across companies and even drawing upon insights from other industries. We can take the opportunity to name one concrete example here on how one can capture opportunities to learn from insights from other companies or adjacent industries. A drug company can apply insights from one type of autoimmune diseases, e.g. Diabetes type I, on the autoimmune disease it is researching, e.g. Rheumatoid arthritis.

We facilitate and democratise adoption of AI technologies, so any company can focus on their core business instead of building out support tools. The first indications for the future of such a platform have been very positive. MainlyAI has already been granted government funding for several high-profile projects.

Pardon, the obvious bias, but we think this is the next really, really big thing. It could be, in line with what Google did for search engines or Apple for app distribution. As crazy as it might sound, we are happy to explain our thesis, based on our own AIStore ™©, which allows storing and improving your existing models and purchasing trained or untrained models, off-the-shelf.

Should you like to join us on democratising AI in the society and help some very innovative companies in their continued growth along the way or, perhaps, you work for a company that is currently considering implementation of new technology – please reach out, we’d love to hear from you.

Regardless – if you are interested in learning more, go check out mainly.ai & follow us on LinkedIn.


[1] https://www.mckinsey.com/business-functions/quantumblack/our-insights/global-survey-the-state-of-ai-in-2021

https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/Global%20survey%20The%20state%20of%20AI%20in%202021/Global-survey-The-state-of-AI-in-2021.pdf

https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/tipping-the-scales-in-ai

https://www.mckinsey.com/business-functions/quantumblack/our-insights/winning-with-ai-is-a-state-of-mind

[2] https://www2.deloitte.com/us/en/insights/industry/technology/artificial-intelligence-roi.html

Summary of 2021

Please watch or read about some of our highlights in 2021.

Looking back at 2021, the first full year of MainlyAI operation, we would like to express our gratitude to our employees, partners, and investors for their contribution to help us approach our vision – To turn data into knowledge and actions, using artificial intelligence.

In 2021, we strengthened the development team that now has competences ranging over UX, data science, AI modelling, database design, and cloud computing. Many thanks for your solid contribution to developing our MIRANDA platform – it is constantly evolving, and today we are ready to show the world its capacity and unique features.

Our already strong project portfolio has been extended with an innovation milleu funded by the Swedish innovation agency, Vinnova, that will benefit those risking to be affected by Diabetes type I and, in the longer run, other auto-immune diseases. We look forward to collaborating in a partner constellation consisting of Diamyd Medical, Leading Health Care, Lund University, National Diabetes Register, and Sahlgrenska University Hospital. The project, named AI for Sustainable Prevention of Autoimmunity in the Society (ASSET), will run over five years with a total budget of 60 MSEK.

We received support from KTH Innovation, and upon completion of the KTH pre-incubator program we partnered up with Busybee, a Stockholm based tech startup that offers occupancy detection systems and venue management to businesses. Finally, we would like to extend a special thanks to our growing network of partners: Astra Zeneca, Busybee, Diamyd Medical, Hitachi ABB, KTH, Leading Health Care, Lund University, National Diabetes Registry, Sahlgrenska University Hospital, Scania, Seco Tools, and Uppsala University.

MainlyAI and Busybee form a partnership

Just in time for Christmas, MainlyAI and Busybee have announced their intention to work together in a partnership to leverage on each other’s know-how and platforms in order to scale up their operations. To begin with, Busybee will stream anonymised data into MainlyAI’s MIRANDA platform, where insights and predictions will be created using artificial intelligence based on occupancy data. As the next step, the participating companies will be able to apply optimisation algorithms on the data streams. Being part of the MIRANDA platform opens up the possibility for privacy-preserving insight-sharing among enterprises to achieve higher precision in predictions and decision-making in shorter time.

MainlyAI in Innovation Milieu ASSET

MainlyAI has, together with Diamyd Medical, Leading Health Care, Lund University, National Diabetes Register, and Sahlgrenska University Hospital, been granted funding by the Swedish governmental funding agency, VINNOVA, in the Innovation milieus in precision health 2021 call. The project, named AI for Sustainable Prevention of Autoimmunity in the Society (ASSET) will run over five years with a total budget of 60 MSEK (40 MSEK from VINNOVA).

The aim of the innovation milieu ASSET is to contribute with personalised precision prevention of autoimmune diseases in the society. In particular, artificial intelligence (AI) techniques will be applied to learn from existing data to identify (i) individuals at risk of developing type 1 diabetes, and (ii) individuals with upcoming/newly diagnosed type 1 diabetes that would benefit the most from precision prevention or early intervention with therapeutic approaches. In doing so, data from the TEDDY (The Environmental Determinants of Diabetes in the Young) study, and other sources will be used.

The ASSET innovation milieu will be coordinated by Diamyd Medical. The application was allocated full funding amount by VINNOVA in strong competition. A complete list of the funded innovation milieus can be found at https://www.vinnova.se/nyheter/2021/09/satsning-pa-innovationsmiljoer-for-mer-traffsakra-losningar-inom-halsa/ (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.

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.