The parliamentary committee for internal market and consumer protection (IMCO) is working on one of the most fundamental principles of the European Union: the single market.

The entire Brexit saga shows once more how difficult it is to leave the single market once you have been a part of it. But also how beneficial it is to be a member of the EU.

After decades of living in a common internal market, many people seem to have forgotten how important it is, and how easy.

But when you travel to for example the United States, and you forget your adapter, you can’t even charge your mobile phone – a problem that never occurs when you travel on the European continent.

The internal market is not only about the free movement of goods and services. It’s also about standards, for products but also for consumer protection. That’s why worldwide people talk about “European standards”, as a label of global quality.

It is the task of the IMCO committee to make sure these European standards are upheld in every single space in Europe.

According to Petra De Sutter (Greens/EFA, Belgium), president of IMCO, “developing a long term strategy for better enforcement of Single Market rules” will be key for the committee’s agenda for the next five years.

Therefore, she says, it is “necessary to continue strengthening the internal market, in particular in a cross-border context, removing unjustified barriers and ensuring that the existing rules are properly and timely implemented and enforced.”

Next to the announced revision of the E-Commerce Directive or new Digital Services Act, the committee will need to have a lot of attention to what De Sutter calls “a single market fit for the digital age”.

“Since emerging technologies, such as artificial intelligence and blockchain, are becoming key drivers for economic development and enhance the value of goods and services, we will be following with the utmost attention any developments in this area”, she said, adding that this is important for both business and consumers.

As a fourth major point for IMCO over the next five years, De Sutter mentions a “sustainable Single Market”. More concretely, she points out that “addressing the needs of a growing circular economy and the integration of environmental concerns into consumer policy will be a key priority.”

A politically-sensitive point that might interfere with the IMCO agenda is the free movement of people.

De Sutter fears that “member states may take disproportionate measures and apply administrative controls and procedures which make free movement more difficult and costly for SMEs going cross-border.”

But all in all, De Sutter thinks that the speedy growth of artificial intelligence will become the biggest challenge, needing “legislation linked to transparency, liability, safety and ethical rules for digital platforms, services and products”.

On the one hand, European companies need access to data for developing AI. On the other hand “consumer protection rules have to ensure that consumers have clear information on how to use AI-enabled products and services, that they have control over data generated by such products and services, and how that data is used”, she said.

IMCO coordinators are: Andreas Schwab (EPP, Germany), Christel Schaldemose (S&D, Denmark), Dita Charanzova (Renew, Czech Republic), Marcel Kolaja (Greens/EFA, Czech Republic), Virginie Joron (ID, France), Adam Bielan (ECR, Poland), Katerina Konecna (GUE/NGL, Czech Republic).

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As we come to the end 2019, we reflect on a year whose start already saw 100 machine learning papers published a day and its end looks to see a record breaking funding year for AI.

But the path getting real value from data science and AI can be a long and difficult journey.

To paraphrase Eric Beinhocker from the Institute for New Economic Thinking, there are physical technologies which evolve at the pace of science, and social technologies which evolve at the pace at which humans can change — much slower.

Applied to the domain of data science and AI, the most sophisticated deep learning algorithms or the most robust and scalable real-time streaming data pipelines (‘physical technology’) mean little if decisions are not effectively made, organizational processes actively hinder data science and AI, and AI applications are not adopted due to lack of trust (‘social technology’).

With that in mind, my predictions for 2020 attempt to balance both aspects, with an emphasis on real value for companies, and not just ‘cool things’ for data science teams.

1. Data science and AI roles continue the trend towards specialization. There is a practical split is between ‘engineering-heavy’ data science roles focused on large production systems and the infrastructure and platforms that underpin them (‘Data/ML/AI Engineers’), and ‘science-heavy’ data science role that focus on investigative work and decision support (‘Data Scientists/Business Analytics Professionals/Analytics Consultants’).

The contrasting skill sets, different mental models, and established department structures make this a compelling pattern. The former has a natural affinity with IT and gains prominence as more models move into production. It has also shown to be a viable career transition from software engineering (such as here, here and here). Conversely, the immediacy of decision support and the need to continuously navigate uncertainty require data scientists working in a consulting capacity to be embedded in the business rather than managed via projects.

We continue to quietly move away from the idea of the unicorn, because just because someone can do something, does not mean he or she should. For all the value of the multi-talented performer, they are not a comparative advantage when it comes to building and scaling large data science teams.

2. Executive understanding of data science and AI becomes more important. The realization is dawning that the bottleneck to data science value may not be the technical aspects of data science or AI (gasp!), but the maturity of the actual consumers of data science.

While some technology companies and large corporations have a head start, there is a growing awareness that in-house training programs are often the best way to develop internal maturity. This is due to their ability to customize content, start from where an organisation is at and align training with identifiable company business problems and internal data sets.

3. End-to-end model management becomes best practice where production is required. As the actual footprint of data science and AI projects in production gets larger, the problems that need to be solved have coalesced into the discipline of end-to-end model management. This includes deployment and monitoring of models (‘Model Ops’), different tiers of support, and oversight on when to retrain or rebuild models when they naturally entropy over time.

Models Ops and the systems that support the activity is also a distinct skill set that is different from that of data scientists and machine learning engineers, driving the evolution of both these teams and the IT organizations that support them.

4. Data science and AI ethics continue to gain momentum and are starting to form into a distinct discipline. Second order effects of automated decision making at scale have always been an issue, but it is finally gaining mind share in the public consciousness. This is courtesy of the prominence of incidents like the Cambridge Analytica Scandal and Amazon scrapping its secret AI recruiting tool that showed bias against women.

The field itself is finding definition around clusters of topics, with activity around automated decision making and when to have a human-in-the-loop, algorithmic bias and fairness, privacy and consent, and longer-term dangers on the path to artificial general intelligence.

Of particular note is the interaction between data science and global privacy regulations. GDPR has been in effect as of mid-2018, and there are now limits on data processing and profiling, requirements of model transparency and the possibility of organizations that data scientists work for being held accountable for adverse consequences.

Technology usually outpaces regulatory paradigms by a few years, but regulation is catching up. This will cause short-term pain as data science and AI teams learn to work within new constraints, but will eventually lead to long term gain as credible players are separated from bad actors.

5. The convergence of tools causes confusion, due to multiple ways to do the same task, with different groups preferring different approaches depending on their background. This will likely continue to cause confusion as newer entrants to the industry may only see a part of the whole.

Today, you may model on enterprise tools if you work for large organizations that can afford them. You may model in a database environment if you are a DBA with MS SQL Server. You could call machine learning APIs and develop an ‘AI product’ if you are a software engineer. You could build and deploy the same model on cloud platforms such as AWS Sagemaker or Azure ML Studio if you have familiarity with cloud offerings. And the list goes on.

The net result may be fertile ground for misunderstanding and turf wars due to similar functionality being available in different forms. Against this landscape, the organizations that are able to build high levels of trust across disparate technical teams will be the ones who reap the full benefits of the toolkit available today.

6. Efforts to ‘democratize’ and ‘automate’ data science and AI redouble, with parties that over-promise failing. With talent being somewhat elusive (or at least mis-allocated), automated data science and AI is an attractive idea. However, the reality remains that the boundaries of technology only enable certain well specified tasks to be automated.

Taking a typical data science project, there is a lot that goes on around the activity of model building:

1. Choosing the right project, putting together a team with the right mix of skills, communicating the approach, and securing necessary support and money if necessary.
2. Once the project is set to start, selecting how to frame the problem and the approach to take. E.g. should failure prediction be framed as a supervised or unsupervised machine learning problem? Or a system to be subject to simulation? Or an anomaly detection problem?
3. Once you have framed the problem, choosing the right data to use, and choosing the right data not to use — e.g. due to ethical considerations.
4. Processing on the data side to make sure it will not result in an erroneous model. For example, email data actually requires a lot of wrangling to get at the actual message among the headers, tags etc.
5. Once you have the data, generating hypothesis — e.g. in data mining in massive data sets, a lot of work is about deciding what ideas might be worth investigating before going to ‘do the data science’.
6. Build and optimize model. <This is what is being automated>
7. Once you have built and optimized your models (if you chose to use models at all), deciding on whether it is valuable or not.
8. Once you have decided that the work is worthwhile, embedding developed machine learning models into a production system and an established business process. This step alone often takes more time than all the other steps combined.
9. Once the model is deployed, building out future releases to ensure that what is built is fully functioning, tested, and integrated with other systems.
10. Once the entire machine learning system is well tested and performing up to engineering standards, actually interpreting and acting on the output from a data science project.

Just as Wix, Squarespace and other website builders did not put web developers out of business, AutoML and DataRobot will not replace data scientists. (They are however, great tools, and should be marketed as such.)

7. Architecture at the Edge and Fog starts to enter the mainstream. The practical necessity and engineering cost of deploying increasingly large sophisticated models is driving new architecture patterns. This is especially true for both compute and data transfer requirements of real time video analytics, being lauded as the ‘killer app’ for edge analytics. The trend is being supported by both advances in computer vision and new purpose built commercial hardware such as the AWS Deeplens.

8. The hype cycle and deluge of definitions are shifting. It was first focused on “big data”, before moving to “data science” some 5–6 years ago, and 2020 may be the year that all things “AI” could overtake the conversation.

One of the side effects of having to on-board large numbers of newcomers is a simplification of the field, which in the case of data science was reducing it to emphasize more on statistics and machine learning while de-emphasizing other mathematical modelling disciplines such as operations research and simulation.

A similar pattern has started to occur in AI, with the analogous emphasis on machine learning, neural networks and deep learning, often in the context of vision and natural language processing. The de-emphasis currently seems to occur in classical AI fields such as knowledge representation, expert systems and planning, among others.

As a side note, I completely empathize that it is difficult to move to a new field, and the breadth of data science and AI can be overwhelming. What I have found most useful in breaking this wall is seldom more content, it is better navigation. Having someone who can orientate what we know and do not know, and draw a personal roadmap of learning is far more useful than an unordered list of links to learning materials.

9. Competition enters the AI chip market. Nvidia has had a tremendous head start in the market for hardware for deep learning, and currently dominates most of AI in the cloud. While there are significant entrants from Google, Qualcomm, Amazon, Xilinx and multiple startups, competition is still mostly occurring on the margins.

This will eventually change as powering AI is not about ‘just a chip’, but about complete, portable hardware platforms, preferably without vendor lock in. Intel and Facebook’s new chip may being the awaited competition, or it could come from Chinese companies rushing to make own chips as trade war bites. Almost on cue, the second half to 2019 saw Alibaba and Huawei both unveil chips.

10. And finally, it is still easier to teach data science and AI and sell tools than to actually make it work in practice. Creating value from data science and AI is not only hard, but require discussion and consensus beyond the data scientist and machine learning engineer alike.

AI systems are often, at their core, optimization machines. And the question we have only started to ask is “what are we optimizing for?” For all the attention given to “doing things right” in data, modelling and architecture, the arguably harder task is “doing right things” in terms of designing for human-centric experiences and values.

Likewise, data driven decisions need to be taken by senior, non-technical decision makers, often tangled in complex webs of political intrigue, and have often succeeded their entire careers without data science.

On the production front, successful model deployment is but one small part of a product, and can be constrained by a myriad of factors ranging from internal IT environments to archaic regulatory requirements, and all this on top of the inherent uncertainty of working with data. The obsession with ‘models in production’ themselves may also be somewhat misguided, and one of the primary KPIs of data science remains its most elusive — “did you change a mind”?

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AI and ML solutions are already being used by thousands of companies with the goal of improving the healthcare experience. For example, Babylon Health is changing the way we manage and better understand health. Founder, Ali Parsa developed the app in 2013 with a mission of providing accessible and affordable healthcare to every individual on earth. Babylon’s AI system has been designed to understand and recognise the way humans express their medical symptoms and it can interpret symptoms and medical questions through a chatbot interface and match them to the most appropriate service. It can recognise most healthcare issues seen in primary care and provide information on next steps to take.

The conversation around artificial intelligence (AI) and machine learning (ML) in healthcare continues to grow. Research in cutting-edge areas like machine learning continues to demonstrate that computers have the potential to predict outcomes and optimise clinical operations in a wide variety of settings.

Healthcare stands poised for a transformation driven by AI and ML, and fuelled by an abundance of data sources – electronic health records, claims data, genomic sequences, mobile devices, medical imaging, and even embedded sensor data.

• How to build healthcare around IoT

Building a foundation for AI and ML

Data is the fundamental raw material required to power AI and ML systems, and is an essential ingredient that enables healthcare organisations to increase efficiency, improve outcomes, and enhance quality of life for both patients and providers.

While the demands of treating patients and developing new therapies often relegate data collection and analysis to a back burner in healthcare, new tools enable developers to integrate ML and other capabilities easily into the routine process of developing and delivering treatments. Far from being an exclusive province of researchers and technology companies, AI and ML are now accessible to all.

As these use cases expand, success is dependent on several ingredients. First, such initiatives require large quantities of carefully curated, high-quality data, which may be hard to come by in healthcare where data is often complex and unstructured. High-quality data sets are required not only to operate AI and ML-driven systems, but even more importantly, to feed the training models upon which they are built.

Second, these systems need to be optimised for the compute-intensive jobs typically required by AI applications. And finally, IT resources supporting AI applications must comply with industry standards and regulations and adhere to the highest security and privacy standards to protect patient and other sensitive data.

One company that has successfully rooted itself in developing and curating its data is Touch Surgery The company is transforming professional healthcare training through the delivery of a unique platform that links mobile apps with powerful data back-end. Touch Surgery uses cognitive mapping techniques coupled with cutting edge AI and 3D rendering technology to codify surgical procedures. They have partnered with leaders in Virtual Reality and Augmented Reality to work toward a vision of advancing surgical care in the operating room. With over 1 million users, the firm are recording vast amounts of usage data to power their data analytics product, which in turn allows users to learn and practice over 50 surgical procedures, evaluate and measure progress, and connect with physicians across the world.

• New frontiers: Healthcare’s digital path forward

Powering Innovation

A crucial technology that provides storage capacity, compute elasticity, security, and analytic capabilities needed to implement AI and ML – and drive innovation – is cloud computing. Cloud computing platforms make it easy to ingest and process data, whether structured, unstructured, or streaming and simplifies the process of building, training, and deploying machine learning-based models. Healthcare organisations that can use cloud computing to make themselves more efficient and effective will be the most successful in coming years, particularly as the industry shifts to value-based care.

For the National Health Service (NHS), AI and ML are having a huge impact on its ability to cut costs, while improving patient services. The NHS is the UK’s largest employer and health provider. NHS Business Services Authority (NHS BSA), a Special Health Authority and an Arm’s Length Body of the Department of Health and Social Care, provides a range of critical central services to NHS organisations, NHS contractors, patients and the public. As such, the NHS BSA’s call centre staff handle around five million calls per year. The organisation decided to implement a cloud-based contact centre and deep learning chatbot service using Amazon Connect and Amazon Lex to help improve the user experience, reduce call centre load, increase efficiency and cut costs. By moving to the cloud, the NHS BSA has identified around $650,000 in cost savings per annum from a reduction in average call times alone.

Healthcare companies, whether established or new start-ups, are increasingly looking to AI and ML to drive innovation and transformation at their company and across the healthcare industry. These organisations share a common goal of reducing time to discovery and insight, improving care quality and enhancing the patient and provider experience. As the availability and volume of data sources continue to grow, the essential ingredients for AI and ML success will remain the same: high-quality data, cloud computing to remove undifferentiated heavy lifting, and ML services accessible to everyday developers. Once these foundational elements are established, AI and ML have the potential to power more efficient and effective care, enhanced decision making and the ability to drive greater value for patients and providers.

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he science of project planning has something of a tenuous reputation. How often do large oil and gas capital expenditure (CAPEX) projects really come in according to plan? Almost never. Even during this era of digital transformation, project schedule and cost overruns are still the normal course of business, not the exception.

Arguably, the reason for this is less about poor execution and more about how we still struggle to accurately forecast how long these complex CAPEX projects will actually take to complete.

According to a 2017 McKinsey report, the Middle East has one of the most significant project pipelines anywhere in the world, with a total of US$396B of future projects planned across the region — this means there is a lot of money to be made and opportunities to engage new methodologies and approaches.

New approaches using digital planning and risk assessment tools are poised to change oil and gas project economics forever, bringing with them the potential to deliver successful and on-time CAPEX projects while unlocking significant value.

For the first time in the industry, project planning can combine artificial intelligence (AI) and human intelligence to create true risk intelligence. In a nutshell, this is achieved by bringing together historical project data and human expertise. This way, planners and project teams can produce more accurate and fully risk-adjusted schedules for their projects.

If oil and gas companies in the Middle East can use these tools to adapt their scheduling practices to meet the needs of their unique regional environment, the productivity improvements could deliver up to 30% in cost savings – a whopping US$118B.

A wide variety of factors can contribute to project delays and CAPEX mismanagement, but the root cause has less to do with the likes of planning techniques not being fit for purpose and more to do with inaccurate data being fed into those plans.

The tide is finally turning toward more accurate project forecasting with the advent of AI and the simple realization that it takes the expertise of a specialized team to build a plan, rather than a single planner working in a silo.

Next-Generation Risk Analysis
To help address the challenge of developing a meaningful risk model, a more team-centric and collaborative means of capturing risk and uncertainty inputs has been developed, along with more easily consumable and actionable risk reports. Enter the human intelligence element, where a team’s collective expertise is pooled together on a single platform.

Let the Software Compile the Uncertainty Ranges for You
Rather than force team members down the “describe the range of outcomes as a distribution” approach, why not capture such expert opinion through a simple scorecard instead? Simply ask team members to either buy in or push back on the proposed durations.

This approach carries the massive benefit of making the expert opinion and knowledge capture process very fast and easy for contributors, while still retaining the underlying modelling methodology. This approach also ensures that the total consensus of the team is accounted for in the risk model rather than being “the voice of one.”

Relating back to the challenge of owner/EPC contractor alignment, this concept of consensus-based planning helps drive that necessary synergy tremendously, which in turn drives buy-in and, ultimately, the project’s chances of on-time completion.

Use AI to Help Establish Your Risk Register
In addition to more efficiently capturing duration ranges through the approach described above, the second step in the risk model building process is to capture and quantify risk events.

Traditionally, risk events have been tracked in what is known as a project risk register. The modelling challenge arises when linking those identified risks from the risk register into the schedule risk model. Without overstating, this process is treacherous at best, and one that causes huge challenges in project risk workshops.

So instead of identifying risks in isolation of the schedule and then trying to embed them back in, why not provide an environment where risks are identified and scored directly in context of the schedule itself?

Taking this a step further, by leveraging AI, team members can also take advantage of the computer making suggestions as to common risks and their historical impact on similar scopes of work.

AI-Driven Guidance on Risk Event Identification

Rather than team members having to brainstorm from a blank sheet of paper, they can take into account previously realized risks and opportunities from similar historical projects. As new risks are identified, they can be automatically added to the enterprise risk register, ready for subsequent consumption. This self-perpetuating risk management loop is an entirely new and more effective way for an oil and gas company to adopt a more mature outlook on risk.

Risk-Adjusted Forecasting Is Applicable to All Project Stakeholders
Historically, project risk analysis has been available to larger project organizations and, typically, embraced more by business owners than EPC contractors. The advent of next-generation, risk-adjusted forecasting software is opening up the benefits of risk insight to the broader market.
By combining the data mining power of AI and pooled human intelligence, risk modelling is making huge strides forward.

Contractor organizations can now benefit from determining applicable contingency, along with appropriate margins, when developing their commercial bids. In short, contractors can ensure they are more competitive by following this risk-adjusted forecasting approach. Likewise, owners now get more insight into the realism and achievability of contractor schedules and, thus, can react and remediate faster.

In all instances, the benefit of providing an easier means of capturing risk inputs, applying them to a proven approach, and then gaining deeper and more meaningful insight through next-generation risk reporting, is hard to argue against. 

The long overdue collaboration between human and artificial intelligence is finally becoming a reality. By enabling on-time project completion, this culmination of proven practices becomes a perfect union, and has the potential to unlock value across a project’s life cycle.  The end result is that more projects will see the light of day.

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