AI Predictions for 2020
The year 2019 has seen self-driving cars doing quite well. Even the deep fakes video got convincing enough to fool most of us. The virtual assistants have infiltrated many areas telling us when to wake up, workout, tell us the weather, and get driving directions home. Just like any big trend, AI has captured the imagination of everyone, with most of us already tiptoeing into one or the other form of AI.
The next year looks like a breakout year for AI, as it starts to permeate all aspects of our lives. We will see an absolute focus on the AI value, jumping out of the experimentation mode and ground firmly in reality, accelerating adoption.
As per Gartner’s report, artificial intelligence grew 270% in the last four years. The CIOs picked AI as the top game-changer technology. How will all the different trends and AI aspects come together? Here are the bold and practical AI predictions from our experts. We look forward to more advances and surprises in 2020.
- Building better AI - The AI community will continue to make progress in building algorithms that train faster, train with lesser data, and generalize better. The use of newer algorithms for data augmentation, few-shot/zero-shot learning will make the cumbersome deep-learning training process easier, and developments in feature-representations and generative networks will work towards making models more generalizable. The use of complex/hybrid series of algorithms to achieve tasks will help build models that scale for complex "real-world" scenarios. The use of self-supervised methods will hasten the progress of generic models. The availability of generic "out-of-the-box" models in machine-vision and NLP will continue to evolve fast - but the need for building customized models for real-world challenges would remain. The use of multi-agent systems would evolve with the need to move towards more generic intelligence capabilities.
- Making AI "really" work - The use of both cloud based and edge-based technologies would expand, and both these ecosystems would cater to use-cases that make sense for them. The use of micro-services frameworks, auto-scaling, and containerization would continue to deliver scalable AI ecosystems, and the use of edge devices would help bring real-time use-cases alive. Hybrid deployment models would be required in solving many of these complex deployments, and the use of in-memory and distributed storage/processing frameworks would continue to power AI systems increasingly.
- Built-for-AI hardware - The increase in the development of special-purpose AI chips and hardware would allow tighter integration on the system. The use of built-for-AI hardware would open vast possibilities to the amount of processing power that AI algorithms can leverage, and would provide a major leap towards next-generation algorithms and systems.
- Safe, governed, and ethical AI - The AI community will continue to debate and progress on the challenge of governance, privacy, safety and ethics in AI Work on privacy-preserving algorithms and similar techniques might provide some technical answers to these tricky questions. The evolution of some open standards or systems to tackle these issues might accelerate in the near future.
- The demand for AI solutions will continue to outpace the availability of AI talent, and businesses will adapt by enabling more applications to be developed by non-AI professionals, resulting in the socialization of the process. Non-AI practitioners, such as knowledge workers and analysts, who are not skilled AI practitioners (but have great domain expertise), will start to develop rudimentary applications aided by automated AI engines. The onus will be on corporate training programs to retrain/upskill these new practitioners and on IT to enable them with automated AI environments that use AI itself (e.g., machine learning apps to help develops train models w/o having to write code). This is not unlike the historical lifecycle of analytics, and it will similarly benefit everyone in the ecosystem: business will expand their capacity to develop and benefit from AI apps, AI experts will be working on the truly leading-edge applications, and tie newly upskilled non-AI practitioners who will contribute more and have more marketable skills.
- Consumers will gain ground in the fight for their right to own and monetize their personal information. They are already being rewarded to some degree, in the form of discounts, premium services and other perks, from sharing their personal information, but for the most part they have few rights in the process and certainly no ownership of their own data. The future will see them test the waters to take more control of the process and receive even direct monetary compensation in exchange for the data they provide. This will disrupt the mega-billion-dollar consumer data marketing industry, which will have to figure out how to deal with the consumer getting a part of that pie and fuel the birth of intermediary businesses that facilitate the exchange of data for compensation between the consumers and the businesses that want to access and use their data.
- The world economy is going through a pivotal moment with the introduction of Industrial 4.0 and associated technologies. Artificial intelligence, the Industrial Internet of Things (IIoT), will continue to play an important role in this new revolution while associated technology like 5G or blockchain would act as catalysts for its wide adoption across different domains and functions.
- Cyber-physical systems and digital twins would make both remote monitoring and control possible for physical assets. Advancement in hardware (sensors and smart chips) and software/middleware (edge computing) would make such digital transformation implementation feasible within enterprises.
- Human-centred design continues to play an important role in ensuring the wide adoption of intelligent systems to augment and extend human capabilities within organizations. 2020 is going to see a new era of the human-machine confluence with further progress in the areas of deep learning especially in the reinforcement learning and generic adversarial network or remote process automation.
- Research community would continue to be invested in finding out new techniques by which they can explain the algorithmic decisions - transforming the black-box approach of AI to glass-box.
- Privacy would continue to remain a concern for consumer-facing AI applications. We would see a substantial rise in research efforts related to building a privacy-aware AI eco-system and enabling fairness in AI algorithms.
- Partner-centric businesses are going to be the new norm of business and technical advancement. ISVs and technical partners who would provide off-the-shelf niche solutions on top of existing platforms (hardware, computing, storage, etc.) would see a more steep adoption curve within enterprises. They would influence the new technology norms within industries.
- There have been numerous AI (or actually ML) related use-cases in 2019 and lots of lessons learnt including that we are still at very early stage of ML usage especially in business applications, and we need to treat this phase as we did with many new methodologies that came before – identify a right problem, test new approach, identify its applicability, iterate until clear advantage (vs. prior methods) is established.
- AL(ML) will be more clearly defined as a part of broader analytics and will have better-defined application areas and value creation. Lots of companies have been confused about the two, and if and how they are connected, while the companies pushed for many ML tests and use-cases, mostly driven by “artificial” pressure, the “traditional” analytics development took a back seat.
- Lots of individual ML use-cases will give ground to better well thought out usages of “traditional” advanced analytics vs. ML.
- In 2020, most companies will come to their senses and consider AI “craze” not as a singular development but more connected to overall analytics strategy and transformation to optimize their existing analytics efforts while setting up the right infrastructure and governance to expand AL potential.
- Analytics quality and value metrics will become the mainstream KPIs to guide future analytics/AI development and investment.
- RPA and Cognitive Process Mining will become even more important for both back office (accounting) as well as functions (sales, marketing, product development) to not only drive more efficient process execution but also optimize and even totally change how these processes will function in the future.
- Analytics/AI activation/adoption, convincing employees for why they should use analytics/AI will become the main focus and the center of all analytics/AI development and investment.
- A true Human/Machine interaction - leveraging both objective data and analytics-driven insights while at the same time incorporating human intuition and experience to learn from both worlds.
- 2019 saw a clear shift in enterprise resources towards getting the data strategy right before launching large AI projects. Many companies focused on creating enterprise data lakes on the cloud that can help get reliable and good quality data sets in place. We are expecting to see this trend accentuate in 2020, with many centralized analytics CoEs first focusing on getting the data right.
- More and more analytics CoEs will be challenged to prove the ROI and impact of the solutions they create. Organizations will look at incorporating design thinking to ensure that the user is in the middle and problems that are important for the business to get prioritized, thereby improving the adoption of the AI & analytics solutions. The operationalization of the solution will be as important as solution development.
- 2020 will see a renewed focus on innovating Brain-Computer interface with a focus on thought-controlled machines. As better and less intrusive sensors emerge for capturing electrical signals when neurons communicate, AI & ML will play a big role in deciphering these into thoughts and actions that a machine can interpret and perform.
- The debate on data standards and regulatory framework around AI will intensify. In January 2020, the CCPA will come into force, and we can expect that other states in the US will follow with their versions of privacy laws. We can also expect regulatory guidelines to start coming from other regions in the world. The AI industry will need to rally around and create self-regulation that establishes guidelines ahead of the regulators swinging into action.
- Deep Fake technology will continue to evolve very rapidly and will emerge as a big challenge for everything from the entertainment industry to politics. Apps like “Zao” from China, have already demonstrated the rapid pace at which deep fake is progressing, and this will pick up even more speed in 2020. The AI industry needs to fight back with better algorithms to detect deep fakes.
- AI will enable true customer centricity – Most retailers today still have a product-first approach. In 2020, this will flip, with a customer-first approach led by AI. The combination of advanced algorithms and improved capability to comprehend and process omnichannel data elements will enable this transition.
- The impact from data privacy laws – The global push by governments to bring in better data handling and privacy laws, will in the short run, limit efforts around personalization (among other things). However, in the long-term, it will lead to more innovative ways of leveraging the available data within prescribed limits.
- More responsible uses of AI – So far, enterprises; have tend to focus on infusing more and more AI into their different streams of work. This will start plateauing naturally, with more emphasis placed on responsible usage, driven by a combination of better awareness and governmental scrutiny globally.
- People-centric AI: Till now the narrative about AI has largely been about how AI will replace human beings. In reality, several applications of AI are focused on augmenting and improving the way human beings work, rather than replacing their work. And therefore, focusing on the ‘people’ (not just ‘users’) who will use or be impacted directly / indirectly through the applications of AI, will become more important. This will mean focusing a lot more on understanding people, their current scenarios, behavior and needs, and how AI can help them in their goals. This will also mean designing AI solutions that are simple, intuitive and delightful in their experience.
- Ethics in the design of AI: While there has been a lot of talk around the role of ethics in the way AI is being used and delivered in various contexts, more often than not, these conversations are post-facto. In the next year, organizations will start using ‘Ethics in AI’ to drive the way new applications of AI are conceptualized and designed from scratch. Given people’s awareness and expectations from issues related to how they use (or are used by) technology, designing for ethical AI will become the norm eventually.