It’s that time of the year when Gartner released their Hype Cycle for Artificial Intelligence identifying trends that accelerate AI-driven transformation.
Advances in machine learning, NLP, computer vision, chatbots and emerging technologies such as generative AI, knowledge graphs and composite AI, inspire companies to invest in AI solutions to create innovative products, improve existing products and processes and enhance customer experience. Nevertheless, these are the trends that dominate the Hype Cycle for 2021 and are the key for scaling AI initiatives and speed up the process of moving PoCs into production: Responsible AI, Small and Wide Data, Operationalization of AI initiatives and Efficient use of Data, Models and Compute, reports Gartner.
AI Innovation shows no signs of slowing down, “with an above-average number of technologies on the Hype Cycle reaching mainstream adoption within two to five years,” said Shubhangi Vashisth, Senior Principal Research Analyst at Gartner. What’s more The AI market remains in an evolutionary state, with a high percentage of AI innovations appearing on the upward-sloping Innovation Trigger, states Gartner, among which are Composite AI, ModelOps, Responsible AI, Generative AI, Human-Centered AI, Synthetic Data, etc.
Speaking of trends in Data and AI, we are gearing up for the 6th edition of the Data Innovation Summit – the most influential Data, Analytics and AI event in the Nordics that features state-of-the-art case studies, research, technology and advancements and innovations that drive AI-driven transformation. The speakers are world-leading experts who come from the most innovative companies in the world.
In a series of interviews, we’ve asked our speakers to “glance a bit into the future” and share what they think would be the trends that will dominate the data and AI field in the next few years.
Sustainable AI for sustainability will become a primary focus
Dr. Mahendra Samarawickrama, Senior Manager – Data Science and Analytics at Australian Red Cross:
“The industry has identified the importance of synergy between the development of AI and mobilizing the organizational culture for realising social justice in AI. A greater social diversity, equity and inclusion can be expected in AI projects which enable ethical inclusion, processes and outcomes in AI. The sustainable AI and sustainable development goals will be a primary focus in AI developments that drive business objectives and corporate social responsibilities. Overall, this can be illustrated by the following figure.”
Micha Ben Achim Kunze, Lead Data Engineer at Maersk:
“I feel like we are coming full circle with what is now being called “data-centric AI”. Data was always the fuel for analytics and AI/ML and it will stay that way. Seeing that being re-iterated in the ML/AI field is great as it acknowledges the importance of good Data Engineering work and efficient DataOps practices.
I am excited about the outlook for the next couple of years, where I expect to see an even bigger impact of Data Engineering in the data world. I also expect some leaps in our practices and tooling that will come along with that.”
Human-Centric AI, focus on reducing compute cost of AI, NLP, Transfer Learning
Nina Hristozova, Data Scientist at Thomson Reuters & Milda Norkute, Senior Designer at Thomson Reuters
“Natural Language Processing (NLP) presents more and more new challenges to the research community every day. Some of the topics that I am personally very passionate about are Human-Centric AI, Monitoring Data Drift and Active Learning. I love these topics as what they all have in common is that they present a fresh way of looking at already established processes.
For example, let’s look at the field of Human-Centric AI. This includes AI Ethics, AI Explainability, Trustworthy AI, etc. Up until now, the main focus when doing data science research and applied data science was to improve the performance of models/tasks as much as possible – even if only by 0.1%. Now with all the incoming regulations and with the advancements in compute power, going to cloud, etc., we can expect greater adoption of AI models. Therefore, the emerging area of HCAI is becoming crucial for the adoption of AI. The more we talk about them, the better for everybody!”
“I think one of the limitations of where and how AI can be applied comes from the computing power needed and, therefore, the cost of creating and maintaining different AI solutions. I expect that future work will focus on exploring how we can reduce the impact of those issues. Data availability to train the models on is another pain point – not many companies have the needed data in the quantity and quality that is required to build powerful, well-performing AI models. So, I would guess that there will be more work and progress in the area of transfer learning, where the application of knowledge gained from completing one task is used to help solve a different, but related problem. More specifically, within the area of NLP at the moment, it is tricky to work with very long text documents – you have a limit on how many tokens you can use, for example, for model training. So I think this will be another area where work will be done to bypass these limits. Finally, I also think that AI Explainability will remain an important topic of research and I personally look forward to doing more work in this area.”
Governance Automation and Hybrid Roles within Data Architecture
Robert Børlum-Bach, Head of Analytics Architecture TV2 Danmark:
“With technological restrictions from e.g., Apple and juridical requirements such as GDPR and ePrivacy, the focus for data collection is changing from cookies and end-clients to server-side and first-party data. This only creates a greater and urgent need for clear data governance and data responsibilities. The business needs to have defined processes for handling data outages – not a nice to have, but a need to have this day today.
Having processes, stakeholder contracts and governance automation that can support these changes, will (looking at the crystal ball) be an important business commodity. And hybrid roles within data architecture, legal (DPO) and business understanding will be highly sought after.”
There we have it — some of the future predictions and trends that will drive AI-Driven Transformation, summed up from the Data Innovation Summit 2021 speaker interviews. We’ll certainly keep a keen eye on the upcoming developments in Data, Analytics and AI, and eagerly anticipate how things will develop on the AI market.