Mindway Ai site

Mindway AI’s Gambling Addiction Education

Happy to share the initiative of Mindway AI for gambling addiction education based on neuroscience. Mindway AI is the winning start-up of Alpha Hub 2018.

They have launched a series of 3 webinars, taking a look behind the scenes at the research that is the foundation of their solutions.

Addiction in gambling: how can neuroscience and AI help?” is the first webinar in a series of three webinars on neuroscience, problem gambling, and artificial intelligence. The webinar gives insight into what happens inside the brain during healthy and problem gambling. Applying neuroscience to understand addiction at the brain level and embracing the root cause opens up new ways of detecting and preventing problem gambling, and artificial intelligence makes it possible to apply these insights at scale.

You can watch all 3 of them in the Webinar’s section on Mindway AI’s website. The other 2 topics are:

Keeping Gamblers safe: Harnessing the Benefits of Neuroplasticity

 

 

Quantum Computing – The next paradigm shift in computing?

Over the past 50 years, the power of computers has grown exponentially as the size of transistors has decreased. This has been characterised by ‘Moore’s law’ which states that the number of transistors on a computer chip doubles every 18 to 24 months.

However, there is now some evidence that Moore’s law is beginning to slow down. Does a new type of computing, quantum computing, offer the opportunity to continue the exponential growth in computing power by harnessing the power of quantum mechanics?

What is a quantum computer and what can it be used for?

A classical computer stores information as ‘bits’ which can exist in one state at a time, either a 1 or 0. In contrast, quantum computers store information as ‘qubits’ which can exist as superposition of both states simultaneously. Quantum computers exploit this property to run millions of computations simultaneously which results in them being able to solve certain types of problems significantly quicker when compared to classical computers.

It’s important to note that quantum computers will not make classical computers obsolete, but instead will offer quicker, more detailed solutions to problems that classical computers particularly struggle with today. These areas include:

  • Optimisation problems – This type of problem involves finding the best possible solution from all of the feasible solutions, and particularly applies to areas such as logistics and the financial services.
  • Quantum cryptography – Quantum computers are also strong enough to break existing encryption and can be used to send theoretically unbreakable communications. This are is of particularly interest to major world governments, particularly China and the US.
  • Data modelling/forecasting – Quantum computers would provide significant advancements in the fields of data analytics and data modelling/forecasting. This has particularly positive implications for areas within AI such as computer vision and robotics.
  • Quantum random number generation – Quantum computers can produce truly random numbers. This is particularly useful in areas such as gambling, statistical sampling, computer simulation and cryptography.

How far away is quantum computing?

The main aim of quantum computing research is to reach what’s called ‘quantum supremacy’. This is the point where quantum computers are able to perform tasks that classical computers are not able to complete in a realistic time frame.

Google claimed to have achieved this feat in October 2019, when they said they had completed a random sampling problem in 3 minutes and 20 seconds that would take a classical computer around 10,000 years. This was disputed by other scientists at IBM but it is likely that companies will display further examples of quantum supremacy over the next few years.

Despite these recent advancements, most of the research still suggests that a commercially viable quantum computer is still 5-10 years away. Currently the most powerful quantum computer available for use, IBM’s 14th quantum computer, contains 53 qubits, but a quantum computer that would be able to offer a real advancement compared to existing technology would require thousands of qubits. There are also a number of technical challenges that hold back the progress of quantum computing. For example, the hardware needs to be cooled to almost absolute zero and must have no outside noise or interference.

Quantum computing is nearing the peak of inflated expectations on Gartner’s 2019 Hype Cycle for AI

Source: Gartner / Forbes

Some organisations however, claim that quantum computing is much closer than many realise. One start-up, equal1.labs, have suggested that they could have a commercially viable quantum computing chip ready by 2022.

Given how quickly the technology is advancing, and with an increasing influx of funding from venture capitalists, it’s possible that quantum computing may become a reality sooner rather than later.

What are companies doing now?

Some major tech companies are beginning to offer cloud-based quantum computing services that can be used to test and develop quantum algorithms on their quantum hardware.

Some of the organisations offering these services include:

  • Amazon Braket – A service that allows users to develop quantum algorithms, and then run them using different quantum hardware technologies.
  • Microsoft Azure Quantum – A full-stack, open cloud ecosystem that allows for developing and running quantum algorithms.
  • IBM Quantum Experience – A service that allows users to study quantum computing and run quantum programs via IBM’s quantum cloud services.

Despite quantum computing still being in its relative infancy, several large corporations are already investigating potential uses by taking advantage of the cloud-based quantum computing services on offer:

  • JPMorgan are looking to use quantum computing technology to improve their fraud detection services and reduce staff hours required on tasks that could be automated.
  • Several biopharma start-up companies are studying the possibility of using quantum computing in the process of drug discovery and development. This could lead to significant advancements in the treatment of diseases.
  • QC technology could be used in modelling and predicting weather patterns. Organisations such as the UK’s Met Office investigating possible uses in this area.
  • Volkswagen are using quantum computing technology to optimise bus routes in cities around the world.
  • Airbus are applying quantum computing technology to optimise aircraft loading and fluid dynamics.
  • Barclays devised a quantum computing initiative working group in 2017 in order to develop a strategy that can take advantage of future quantum computing advances.

What does the future look like?

Quantum computing has the potential to revolutionise a number of industries and forward-thinking companies are already investigating ways they can leverage this technology when it becomes more commercially viable.

This may not happen for a few years, but we may see some more useful advancements emerging from the major tech companies sooner than most people think.

If you’re working on quantum computing and feel that you could offer useful insight or expertise, we’d like to hear from you.

Accelerating the Adoption of AI – Our First Community Event

On the 11th October, we hosted our first community event to present the Alpha Hub innovation platform in our Cluj office in Romania titled “Accelerating the Adoption of AI”. This event was aimed at beginning to create a virtual community of start-ups, academics, investors and corporate partners. The specific purpose of the event was to promote discussion and knowledge around Artificial Intelligence (AI) initiatives while connecting different parts of the local community. We brought together experienced researchers and practitioners in the field, while also sharing what we do in our European division, Paddy Power Betfair.

We broke the evening into two parts. Firstly, a presentation on the Alpha Hub platform initiative by Flutter Technology to connect to the international start-up ecosystem and present the collaboration and growth opportunities we could offer. The second part was an in-depth panel discussion with specialists working with or researching AI technologies, especially Machine Learning/Deep Learning and Knowledge Representation.

Guest speakers

PPB Participants

  • Oana Iacoviță – Head of Data Delivery at Betfair Romania Development, focused on delivering Business Intelligence, Advanced Analytics, Data Platforms and Automation Implementations for Paddy Power Betfair.
  • Anca Lazăr, Sorin Circo – experienced Data Software Engineers, working on data processing, processes orchestration and productionizing ML Models.

The Panel Talk

Technical Overview

Venturing into the areas of expertise of the respective panel speakers, the discussion covered a wide range of topics in the realm of AI with a focus of some of the main technical areas, challenges and what accelerating the adoption of AI means to Europe.

Firstly Răzvan Florian explained the direction of AI research in the Romanian Institute for Science and Technology (RIST) institute. With 22 researchers working in Machine Learning (ML), with special focus on Deep Learning (DL), they are divided into two groups. The first group is automating the creation of web pages and studying curiosity-based learning (similar to how children learn exploring the world around them and discovering new learning models). The second one is studying the application of Non-Euclidian mathematics in DL to improve convergence methods. The latter can be applied to text, audio and video processing. The institute has also initiated two spin-offs in the past based on their research.

Although ML and DL are underpinning most of the practical applications of AI, Adrian balanced the view with a fresh perspective: there is not a single “party” in the AI world, but two main ones. One is based on statistics and extracting patterns out of data, including Bayesian networks, NLP, and machine learning. The star of the show is Deep Learning, something that has solved the classification problem based on CNN (Convolutional Neural Networks) and RNN (Recurrent Neural Networks). One of the disadvantages of DL he pointed out is the explainability issue and the different outputs that you can get by applying different algorithms on the same data. The other “party” is called Knowledge Representation – representing knowledge in a logical format and rationalizing based on that to solve complex problems. One of its branches, XAI (Explainable AI), is aimed at making the black box models of DL transparent. University research in AI at UTC-N covers both domains of research.

On the other hand, Răzvan confirmed that advanced DL algorithms have loads of applications in business environments and there is collaboration openness from the RIST institute. One of the main conditions of applying Dynamic Programming in a company is to have data available with good quality and in large quantity. As quality of the outputs depends of the quality and the quantity of the data that can be made available to algorithms – the more data the higher probability of accuracy. RIST offers two models of collaboration:

  • Consultancy – implementing DL models for business specific needs
  • Research contra equity – applied research directed mostly to start-ups

A concrete application of the RIST research can be found in the automation of interfaces creation that is being pioneered by the AI start-up teleportQH, headed by Paul Brie. TeleportQH was founded 2 and a half years ago, one of its objectives being the automated generation of user interfaces code. From that the next step was trying to generalize to other use cases, an approach that opened a lot of opportunities – evaluation metrics for websites, predictions of usability and website performance etc. Paul Brie has pointed out the challenge his team of software developers had in learning ML at the level of production readiness, as well as the heavy work of curating the data for the algorithms, that takes about 90% of the development time. He has also highlighted the importance of the tight collaboration between research centers and companies looking for innovative solutions. The teleportQH initiative is a clear example that ML  has opened a wide range of possible options that would not have been possible with deterministic programming.

On Challenges in AI

One of the main challenges of ML projects that Paul observed was the collaboration between software programmers and data scientists, or of people with a ruled-based mindset and people used to the word of probabilities that are more risk prone. There is an acceptability limit of the risk in each industry depending of the perception of performance for the specific use case.

The access to talent was identified as a general challenge, with PPB also having challenges in terms of finding senior machine learning engineers. Part of the solution could be found in the collaboration between companies and universities to run together projects at the doctoral level that are specified by a company which also provides part of the funding.

For researchers training, Răzvan Florian has organized a Summer School of Machine Learning in 2018. He has brought to Cluj some of the top 50 researchers in the world, and the program had over 160 students from all over the word. Teachers were from Google Deep Mind, AI Research etc.

Also, in the panel he announced for the first time publicly an initiative to help grow skills in ML: a local study group for developers that can collaborate to solve ML problems (e.g. Kaggle competitions or community projects). With significant learning resources available, he noticed that the challenge is getting yourself motivated to learn and to get over moments where you are stuck with a problem. These are times when a ML community is valuable and can help grow the AI community.

On the Future of AI

Paul showed an optimistic perspective on the future of AI, considering that it will be able to fully replace human work so that people are free to make decisions that cannot be yet made by machines.

With a lot of transformation in the AI space and various online resources available (e.g. Amazon offering on their platform the training resources used for their internal staff), a lot of young people are approaching the domain successfully . This is what Paul Brie found out while searching for talent to hire for teleportHQ.

Paul Brie and the group agreed that AI is one of the systemic and strategic technologies of the future and in Europe we are already behind US and China in the space. We need to catch up if we want to be creators, not only consumers of value. In his view: “the stake is not to make more money, it’s to exist or not in a quite near future”. In 10 years, he says, may be already late. There is a deficit of ML knowledge at European level.

On Funding and the Adoption of AI in Europe

Collaboration was a topic that emerged as a way to advance AI at a local and international (European) level. Answering a question from the audience related to availability of funding for AI start-ups, Paul said that the ecosystem and understanding in Romania and Eastern Europe is scarce. Also, he noted that the duration of ML project release is longer than traditional software development, so it needs stakeholders to understand how the technology works. Răzvan pointed out that the European Commission has an initiative to invest publicly into AI research that has had innovative results. But, as Adrian underlined, the AI budget from the EU is 2% of China’s barring each country’s own investments in AI. And, as he showed, innovations in the US are immediately implemented in China in a race where other countries lag behind.

Therefore, a collective European effort should be directed to AI, Paul says. especially to develop the competencies in people. Through Spherick he will be lobbying for that at the EU level to influence the technical strategy in the next years. By the way the automation power grows due to better algorithms and more data being available the choice is simple: participate or be a spectator of the future.

One of the things we could do better to promote the technology, pointed out by Oana Iacoviță, is for technical people to find an even better way to explain to business the value of ML to stakeholders and convince them to invest in it. Other things worth doing more for the AI ecosystems are sharing knowledge, connecting people in the AI community to drive common initiatives and support each other.

Adrian Groza (UTC-N) has given a good example where other countries than China or US do significant work in AI. For example, the Arab Emirates that have started in 2017 an AI ministry and started an education program with the guidance of Oxford University and engagement from the public and private sectors around the adoption of AI. They have created summer schools for students and further introduced from the 6th grade Python classes to teach children ML. Adrian warned that 40% of people in Romania and generally in Eastern Europe are at risk of their jobs being replaced by AI automation. On a similar note, due to the financial advantages of IT-based jobs compared to other industries, especially in Romania, a lot of young people are attracted to the AI word. As Paul Brie observed, there is in Romania a great pool of talent, and “a lot of minds to be discovered and grown”.

Conclusion

The future is promising if we understand the technology direction and if we educate young people in AI technologies, while empowering them to use them in a smart way. We would again like to thank everyone who attended and gave so much great insight into the current and future of the field and their advice on what it takes for Europe to accelerate the adoption of AI.

Our aspiration is to continue this series as talks, meet-ups and general discussion amongst our growing Alpha Hub community. If you are interest in joining, please submit your details here and we will invite you to the next event.

As always, we have an interest in speaking with companies with capabilities in any of this future mobile technology. At Alpha Hub, part of our mission is to accelerate the adoption of emerging technologies across Flutter Entertainment. If you’re working with innovative technologies that you think could support our global brands such as Paddy Power, Betfair, Sportsbet or FanDuel, we’d love to hear from you.

Cluj Startups Scene

Start-up Partnership Models – Finding the Right One for You

There are many different start-up partnership models that corporates can take to identify and partner with start-ups. However, there is certainly no one-size-fits-all approach and most organisations will trial several different methods to find the one that works best for them.

It is important to keep in mind that these models are often just the starting point of the relationship, and as we’ve covered previously, there are lots of things that start-ups and corporates can do to give the partnership the best chance of success.

In this article we’ll explore some of the most popular start-up partnership models, outlining the pros and cons of each.

Start-up Partnership Models

Scouting Missions

The simplest method for identifying potential start-up partnerships is proactively scouting for relevant external companies. This can either be done via an internal scouting or partnerships team or working in conjunction with an external agency.

Scouting is a low-cost approach to start with, but it can be difficult to achieve significant results without a clear vision of the types of start-ups you’re looking for, and a strong network to help you surface the best companies.

Hackathons

The hackathon is a frequently used buzzword within corporations and innovation hubs. This approach enables the collaboration of people from multiple backgrounds with various skill sets, all with the same objective of solving a problem facing a corporation. For example, this could be from creating a new mobile application, the implementation of Virtual Reality or “hacking” away at old processes and ways of working.

This approach is typically used by internal teams, but there is tremendous value to be had by opening participation to third parties who can offer a fresh perspective to solving business problems or developing innovative new propositions.

Companies such as BeMyApp can help facilitate connections to a broader ecosystem of start-ups and developers, to help include a wide spectrum of skills and experience in your hackathon.

Challenge / Prize

In the “challenge” model, companies or organisations set out a specific challenge with well-defined parameters and open it up to the public to find the solutions, with a predetermined prize for the best submissions. As there is very little financial outlay for the corporation if the requirements of the challenge are not met, this is a very low risk approach for corporates.

Corporate challenges have been employed across various industries and can often provide innovative solutions to a specific issue which it has been impossible to solve internally. A challenge prize seeks third party guidance and direction, in some cases, providing monetary rewards and exposure/recommendations for the successful start-up.

There are numerous examples of companies that have used the “challenge” model, most famously perhaps is Netflix who offered $1m to the team that most improved their recommendation engine, or more recently, the challenge from HENRi @Nestle “tackling action to replace plastic straws”.

Corporate Accelerator

The corporate accelerator / incubator approach has become increasingly common over the past few years. In this model, start-ups are typically offered a place on a “fast-track” program, with a view to accelerating the start-up’s development. Many corporations differ in what the “fast track” program entails, but it could include coaching, investor networking, mentoring and the use of office space. A stumbling block for start-ups with this approach can be time constraints with the length of the program and in some cases exchanging a % of ownership for equity.

Some established corporate accelerators include:

  • The UniLever Foundry
  • Barclays Accelerator, powered by Techstars
  • Wayra, by Telefonica

Corporate Venture Capital

Corporate venturing gives companies a mechanism to invest funds directly into external start-ups. In this model it is common for investors to look for later stage start-ups, which have demonstrated viability and generally have an established product with a strong market presence.

These investments can be based purely on expected financial gain, or more strategic in nature, such as interest in a particular technology. A start-up can benefit by leveraging the corporates brand, network of connections and ecosystem of developed products.

Two of the most active corporate venture funds are Intel Capital and Google Ventures. iZettle, a business focusing on mobile payments for small businesses, benefited from investment from Intel Capital. Prior to this investment, the Stockholm-based group was a loss-making company, however went on to be acquired by Paypal for over £1bn.

Likewise, another well-known beneficiary of corporate venture fund is Uber. Google Ventures placed a $258 million bet on Uber in 2013, an investment that is worth over twenty times that today.

Creating the best start-up environment

Finding the most suitable partnership model for your organisation will depend on your available resources, risk appetite and objectives. And with most of these it is possible to start small, test the waters and ramp up your efforts once you start to see success.

Many companies rush into elaborate programs with much fanfare, only to shut them down soon after when the results don’t materialise.

At Alpha Hub, we’re looking to cultivate a community of start-ups, technologists and investors with ideas that can help move the sports betting and gaming industry forward. If you’d like to be part of it, get in touch or sign up below.

Top Tips for Successful Corporate Start-up Partnerships

Over the past few years there has been a huge increase in corporates launching start-up incubators, accelerators, partnership programs and corporate venture funds, all with a view to getting more access and exposure to the tech start-up ecosystem.

And as a start-up you are almost certainly hoping to sell to, partner with or be acquired by a big corporate, so these relationships are mutually beneficial.

But they are also notoriously difficult to get right.

Corporate incubators and accelerators have a bad reputation for failing to add value either to the start-ups they work with, or the corporates they serve. All too often they are perceived simply as a PR exercise, or a last-ditch attempt to avoid being left behind by innovative new entrants to the market.

However, this needn’t be the case. Managed properly, corporate start-up partnerships can be incredibly valuable for both.

Below we’ve laid out some tips to keep in mind to help foster successful relationships:

Corporate partnership tips for Start-ups:

1. Find your advocates

Navigating a large corporate can be incredibly difficult. People’s job titles often tell you very little about what they actually do, or whether they have the authority to sign off on a project. So, getting to the right people in the first place can be a challenge.

If you can build advocates internally, they can help you navigate the org charts and make introductions to the most appropriate stakeholders. Beyond making introductions, strong advocates will also champion your start-up during internal discussions and give you honest feedback on what the purchasing cycle looks like.

Many corporates have innovation teams or partnership programs where employees are incentivised to establish relationships with third parties, so it is worth building links with these teams.

2. Be patient

The normal purchasing cycle at large companies can be very slow. Once you’ve eventually found the right stakeholders who like your product, you’ll still be up against other internal priorities to get on the roadmap. There could also be multiple levels of approvals to get the project signed off.

If there is a tight annual planning cycle your only option might be to make sure you’re included in next year’s budget, then sit tight.

Nine or ten months between initial meeting and actually doing a project is not uncommon. “Not yet” doesn’t necessarily mean “no”, so stick with it.

3. Don’t bet the house on it

An investor at Dublin Tech summit earlier this year told me “start-ups die waiting for corporates”. While you will definitely require patience, be realistic about your chances of any individual deal coming good. If possible, try to get honest feedback about timelines, and always prepare for the worst.

A change of priorities, new stakeholders getting involved or delays getting funding approved can kill your chances of getting a deal signed in an instant.

4. Be careful about taking “strategic” investment

Getting investment from a corporate accelerator or venture fund might sound like great news, but you should weigh the offer carefully and consider the downsides.

You would like to imagine that the company that offers you investment could also go on to be your first customer, but all too often that is not the case and it can lead to some awkward conversations with other potential customers.

If your investors don’t like your product enough to use it, it doesn’t send a great signal.

5. Don’t over promise

A certain amount of salesmanship is expected to help get you through the door, but in the long run you are far better off giving an honest portrayal of your product or service. As a fledgling start-up, it is likely that what you are doing is difficult, otherwise someone else would probably already be doing it.

Don’t be afraid to open up about some of the difficulties you’ve had, or challenges you expect to face. If you’re hoping to build a true partnership with a corporate, they should be able to help you through these challenges, rather than be put off by them.

At Alpha Hub we’re far more likely to engage a start-up who is honest about what they can and can’t do from the outset. It’s harder to believe a company that tells you they can solve all your problems.

Start-up partnership advice for Corporates:

1. Think about what you can give, not just what you get

Working with early stage start-ups has to be different from normal supplier relationships. View it as a potential long-term partnership from the outset and be willing to invest some time and energy to help them progress.

Thinking about where the start-up could be in a year or eighteen months’ time, and how you can help them get there will ultimately be valuable for both of you.

This extends to things like negotiating contracts. Haggling over the pennies won’t make much difference to a big corporate but could have a real impact on the start-up.

2. Include the right people from the outset

Try to bring the right stakeholders and decision makers into the conversation as soon as possible. This will help to speed up the process further down the line and will give you a much better idea whether the partnership has any chance of success.

The “right stakeholders” includes people like procurement, vendor management or finance teams who will also have a say. Running start-ups through your normal procurement and due diligence processes will likely throw up some red flags. Giving people visibility of what you are planning early on will help to smooth the path to getting a deal done.

3. Beware the corporate immune system

“Not invented here” syndrome is real. There will always be teams or individuals who are wedded to their ideas and aren’t interested in third parties who are doing things differently.

Buy-in from senior management for your start-up partnership initiatives can help to overcome this somewhat, but ultimately if you have to convince them and they’re not excited by it, you’re probably talking to the wrong people.

Finding start-ups that help address pre-agreed business challenges will likely be far more successful than trying to get traction for random start-up enquiries.

4. Be upfront about the process

As above, a slow decision-making and procurement process can have a seriously detrimental effect on start-ups. Be open with them about what needs to happen to get a deal done, how long it might take and the likelihood of it going ahead. If it is going to take six months, try to tell them that from the outset so they can plan accordingly.

Corporate Start-up partnerships – Parting thoughts

Dealing with the unprecedented rate of change of technology is undoubtedly a challenge for corporates and building relationships with start-ups is one method to help overcome it. Successful partnerships give you an opportunity to explore solutions to difficult business challenges without diverting attention away from your existing product roadmap.

By keeping in mind the tips above, you can hopefully build relationships that are beneficial for all involved.

Do you have other tips for building successful corporate start-up partnerships? We’d love to hear them in the comments.


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