2019 was an interesting year for the technology industry. Many technologies were hyped heading into the year; however, their paths of adoption took different directions:
- Adoption of AI/ML continued increasing, with newer breakthroughs and areas of application – especially in biology, medicine and research.
- 5G, while discussed broadly by technology vendors and the media, encountered a few challenges with infrastructure, security and adoption.
- Enabling intelligence on the edge gave rise to the new term AIoT – renewing vigour and focus towards enabling the next generation of industrial automation.
With these trends in mind, we predict that 2020 will be a year of convergence and course correction for younger technologies, with an increasing trend towards sustainability and greener solutions.
Blockchain finding its niche in the secure, distributed data store
While 2019 saw quite a few interesting applications of Blockchain, there have been two main challenges to its adoption: 1. Lack of standardisation (platforms, specification, interfaces, etc), and 2. The fact that benefits of Blockchain are realised once a majority of the collaborating providers in a chain all start using the same – or interoperable – platform(s).
The current major players in the platform space all have their own standards for their products – design, components, contracts and implementation – thereby tying an early adopter down to a single product. This lack of standardisation has been an area of significant focus/attention recently. ISO, IEEE have both started standards initiatives that would be ready by 2021 – and we would expect the platform providers to start supporting these standards once they hit early access (hopefully by 2020).
In parallel, enterprises today have started adopting Blockchain in a phased manner – the approach now – from the enterprise’s PoV – is to design the to-be state of the information architecture in a future-proof manner (keeping the application code as product-neutral as possible), and realise the true benefits of interoperability and data-sharing once the partners start their implementations.
With the purchase of a few Blockchain products by the existing stalwarts in the market, cloud support and integration with other existing technologies are also on the increase. With all of the above, we believe 2020 would be the year Blockchain enters mainstream adoption as the distributed store of the future.
AIoT adoption enables more sustainable, greener solutions
2019 saw an increase in an infusion of IoT into existing scenarios - with most of the challenges around adding IoT/sensor capabilities and enabling intelligence on the edge being resolved (this fusion of IoT and AI is now known as AIoT). While the original purpose behind enabling these capabilities may have been to do with early prediction of faults or optimising usage patterns for efficiency, the large volume of data now available from these devices/sensors has opened up new avenues of exploration/optimisation.
The evolution of IoT into AIoT progressed in 3 distinct stages:
- Enabling core capabilities on the edge – these included basic sensor development, integration with available devices, etc.
- Collecting the data generated from these sensors and storing them in a structured form on a central data store – typically on the cloud
- Realising the synergy between AI/ML and IoT and combining them together into AIoT (2019)
Focus in this area has also been evolving along with the core technology itself – shifting towards applications of AIoT (away from initial device capabilities/integration). In other words while IoT provided access to a large base of information (‘here’s the data’), AI/ML has brought in the intelligence and decision making (‘here’s what you can do with it’, and ‘here’s where you are inefficient’).
We believe that in 2020 this continued focus on AIoT adoption, combined with the ability to move decision making to the edge will drive a responsible, sustainable and greener approach to energy consumption.
Focus shift in the field of AI/ML from ‘narrow’ to multi-modal (or ‘general’) intelligence
2019 saw an increase in adoption of AI/ML solutions in newer and previously unexplored areas. This will continue into 2020 as algorithms get more intelligent. However, the scope of existing machine ‘intelligence’ is still too narrow, and focussed mostly on single objectives. To put it simply: the engine that is classifying a picture of a ‘cat’ doesn’t really understand what exactly ‘is a cat’ (semantic information that is understood by a different ‘narrow’ engine that only understands the semantic concept of a cat).
There are already efforts underway to create multi-modal intelligence in the industry. We at Mindtree are also looking at implementations that can combine natural language with visual cognition. The goal is to expand narrow-ness of an AI solution, and to enable transferability so that we can prove understanding – in the example above, that would mean that an algorithm will eventually be able to recognise a picture of a cat and understand what that actually means – much like how humans think.
We believe that in 2020, the focus of AI would shift towards multi-modal intelligence – such an achievement would open the doors to many more uses for AI/ML in future.
Humans’ trust in AI/ML solutions increases
While 2019 has seen an increased adoption of AI/ML across the industry, there have also been quite a few ‘unintended consequences’ – incidents leading to an overall ‘trust crisis’ with decisions put forward by algorithms. Algorithms trained on data captured over the past few years naturally reflect biases inherent in the data – but when evaluated through a more evolved values-set, are obviously found lacking. Making AI/ML solutions interpretable has hence been an area of interest – if we can understand or interpret the steps an algorithm took to arrive at a decision, we would be able to decide the limitations of the algorithm itself, or the missing gaps in the data that the algorithm was trained on.
In 2020 we will see two things help to address these limitations. Firstly, we will see increased regulatory support to ensure AI/ML follow certain principles. Secondly, solutions will be built to give an outside-in view of black box algorithms, helping humans better understand black box algorithms and thus alleviating the current trust issues.
Rajamani Saravanan, Chief Architect, Mindtree