We are back with Part 2 of our analysis of intriguing AI trends in 2018 as promised in our last post. We covered the first nine trends in part 1 of this two-part prediction series. To refresh your memory, these are the trends we are betting on.
- Artificial General Intelligence may gain major traction in research.
- We will turn to AI enabled solution to solve mission-critical problems.
- Machine Learning adoption in business will see rapid growth.
- Safety, ethics, and transparency will become an integral part of AI application design conversations.
- Mainstream adoption of AI on mobile devices
- Major research on data efficient learning methods
- AI personal assistants will continue to get smarter
- Race to conquer the AI optimized hardware market will heat up further
- We will see closer AI integration into our everyday lives.
- The cryptocurrency hype will normalize and pave way for AI-powered Blockchain applications.
- Advancements in AI and Quantum Computing will share a symbiotic relationship
- Deep learning will continue to play a significant role in AI development progress.
- AI will be on both sides of the cybersecurity challenge.
- Augmented reality content will be brought to smartphones.
- Reinforcement learning will be applied to a large number of real-world situations.
- Robotics development will be powered by Deep Reinforcement learning and Meta-learning
- A rise in immersive media experiences enabled by AI.
- A large number of organizations will use Digital Twin
Without further ado, let’s dive straight into why we think these trends are important.
10. Neural AI: Deep learning will continue to play a significant role in AI progress.
Talking about AI is incomplete without mentioning Deep learning. 2017 saw a wide variety of deep learning applications emerge in diverse areas from Self-driving cars, to Beating Video Games and Go champions, to Dreaming, to Painting pictures, and making scientific discoveries. The year started with Pytorch posing a real challenge to Tensorflow, especially in research. Tensorflow countered it by releasing dynamic computation graphs in Tensorflow Fold. As deep learning frameworks became more user-friendly and accessible, and the barriers for programmers and researchers to use deep learning lowered, it increased developer acceptance. This trend will continue to grow in 2018.
There would also be improvements in designing and tuning deep learning networks and for this, techniques such as automated hyperparameter tuning will be used widely. We will start seeing real-world uses of automated machine learning development popping up. Deep learning algorithms will continue to evolve around unsupervised and generative learning to detect features and structure in data. We will see high-value use cases of neural networks beyond image, audio, or video analysis such as for advanced text classification, musical genre recognition, biomedical image analysis etc.
2017 also saw ONNX standardization of neural network representations as an important and necessary step forward to interoperability. This will pave way for deep learning models to become more transparent i.e., start making it possible to explain their predictions, especially when the outcomes of these models are used to influence or inform human decisions.
2017 saw a large portion of deep learning research dedicated to GANs. In 2018, We should see implementations of some of GANs ideas, in real-world use cases such as in cyber threat detection. 2018 may also see more deep learning methods gain Bayesian equivalents and probabilistic programming languages to start incorporating deep learning.
11. Autodidact AI: Reinforcement learning will be applied to a large number of real-world situations.
Reinforcement learning systems learn by interacting with the environment through observations, actions, and rewards. The historic victory of AlphaGo, this year, was a milestone for reinforcement learning techniques. Although the technique has existed for decades, the idea to combine it with neural networks to solve complex problems (such as the game of Go) made it widely popular. In 2018, we will see reinforcement learning used in real-world situations. We will also see the development of several simulated environments to increase the progress of these algorithms. A notable fact about reinforcement learning algorithms is that they are trained via simulation, which eliminates the need for labeled data entirely. Given such advantages, we can see solutions which combine Reinforcement Learning and agent-based simulation in the coming year. We can expect to see more algorithms and bots enabling edge devices to learn on their own, especially in IoT environments. These bots will push the boundaries between AI techniques such as reinforcement learning, unsupervised learning and auto-generated training to learn on their own.
12. Gray Hat AI: AI will be on both sides of the cybersecurity challenge.
2017 saw some high-profile cases of ransomware attack, the most notable being WannaCry. Cybercrime is projected to cause $6 trillion in damages by 2021. Companies now need to respond better and faster to these security breaches. Since hiring and training and reskilling people is time-consuming and expensive, companies are turning to AI to automate tasks and detect threats. 2017 saw a variety of AI in cyber sec releases. From Watson AI helping companies stay ahead of hackers and cybersecurity attacks, to Darktrace—a company by Cambridge university mathematicians—which uses AI to spot patterns and prevent cyber crimes before they occur.
In 2018 we may see AI being used for making better predictions about never seen before threats. We may also hear about AI being used to prevent a complex cybersecurity attack or the use of AI in incident management. On the research side, we can expect announcements related to securing IoT.
McAfee has identified five cybersecurity trends for 2018 relating to Adversarial Machine Learning, Ransomware, Serverless Apps, Connected Home Privacy, and Privacy of Child-Generated Content.
13. AI in Robotics: Robotics development will be powered by Deep Reinforcement learning and Meta-learning
Deep reinforcement learning was seen in a new light, especially in the field of robotics after Pieter Abbeel’s fantastic Keynote speech at NIPS 2017. It talked about the implementation of Deep Reinforcement Learning (DRL) in Robotics, what challenges exist and how these challenges can be overcome. DRL has been widely used to play games (Alpha Go and Atari). In 2018, deep reinforcement learning will be used to instill more human-like qualities of discernment and complex decision-making in robots.
Meta-learning was another domain which gained widespread attention in 2017. We Started with model-agnostic meta-learning, which addresses the problem of discovering learning algorithms that generalize well from very few examples. Later in the year, more research on meta-learning for few shot learning was published, using deep temporal convolutional networks and, graph neural networks among others. We’re also now seeing meta-learn approaches that learn to do active learning, cold-start item recommendation, reinforcement learning, and many more. More research and real-world implementations of these algorithms will happen in 2018.
2018 may also see developments to overcome the Meta-learning challenge of requiring more computing power so that it can be successfully applied to the field of robotics. Apart from these, there would be improvements in significant other challenges such as safe learning, and value alignment for AI in robotics.
14. AI Dapps: Within the developer community, the cryptocurrency hype will normalize and pave way for AI-powered Blockchain applications.
Blockchain is expected to be the storehouse for 10% of the world GDP by 2025. With such a high market growth, Amazon announced the AWS Blockchain Partners Portal to support customers’ integration of blockchain solutions with systems built on AWS. Following Amazon’s announcement, more tech companies are expected to launch such solutions in the coming year. Blockchain in combination with AI will provide a way for maintaining immutability in a blockchain network creating a secure ecosystem for transactions and data exchange. AI BlockChain is a digital ledger that maximizes security while remaining immutable by employing AI agents that govern the chain. And 2018, will see more such security solutions coming up. A drawback of blockchain is that blockchain mining requires a high amount of energy. Google’s DeepMind has already proven that AI can help in optimizing energy consumption in data centers. Similar results can be achieved for blockchain as well. For example, Ethereum has come up with proof of stake, a set of algorithms which selects validators based in part on the size of their respective monetary deposits instead of rewarding participants for spending computational resources, thus saving energy. Research is also expected in the area of using AI to reduce the network latency to enable faster transactions.
15. Quantum AI: Convergence of AI in Quantum Computing
Quantum computing was called one of the three path-breaking technologies that will shape the world in the coming years by Microsoft CEO, Satya Nadella. 2017 began with Google unveiling a blueprint for quantum supremacy. IBM edged past them by developing a quantum computer capable of handling 50 qubits. Then came, Microsoft with their Quantum Development Kit and a new quantum programming language. The year ended with Rigetti Computing, a startup, announcing a new quantum algorithm for unsupervised machine learning.
2018 is expected to bring in more organizations, new and old, competing to develop a quantum computer with the capacity to handle even more qubits and process data-intensive large-scale algorithms at speeds never imagined before. As more companies successfully build quantum computers, they would also use them for making substantial progress on current efforts in AI development and for finding new areas of scientific discovery. As with Rigetti, new quantum algorithms would be developed to solve complex machine learning problems. We can also see tools, languages, and frameworks such as Microsoft’s Q# programming language being developed to facilitate quantum app development.
16. AI doppelgangers: A large number of organizations will use Digital Twin
Digital twin, as the name suggests, is a virtual replica of a product, process or service. 2017 saw some major work going in the field of Digital twin. The most important being GE, which now has over 551,000 digital twins built on their Predix platform. SAP expanded their popular IoT platform, SAP Leonardo with a new digital twin offering.
Gartner has named Digital Twin as one of the top 10 Strategic Technology Trends for 2018. Following this news, we can expect to see more organizations coming up with their own digital twins. First to, monitor and control assets, to reduce asset downtime, lower the maintenance costs and improve efficiency. And later to organize and envision more complex entities, such as cities or even human beings. These Digital twins will be infused with AI capabilities to enable advanced simulation, operation, and analysis over the digital representations of physical objects. 2018 is expected to have digital twins make steady progress and benefit city architects, digital marketers, healthcare professionals and industrial planners.
17. Experiential AI: Rise in immersive media experiences based on Artificial Intelligence.
2017 saw the resurgence of Virtual Reality thanks to advances made in AI. Facebook unveiled a standalone headset, Oculus Go, to go on sale in early 2018. Samsung added a separate controller to its Gear VR, and Google’s Daydream steadily improved from the remains of Google Cardboard.
2018 will see virtual reality the way 2017 saw GANs – becoming an accepted convention with impressive use cases but not fully deployed at a commercial scale. It won’t be limited to just creating untethered virtual reality headgears, but will also combine the power of virtual reality, artificial intelligence, and conversational platforms to build a uniquely-immersive experience. These immersive technologies will come out of conventional applications(read the gaming industry) to be used in real estate industry, travel & hospitality industry, and other segments. Intel is reportedly working on is a VR set dedicated to sports events. It allows a viewer to experience the basketball game from any seats they choose. It uses AI and big data to analyze different games happening at the same time, so they can switch to watch them immediately. Not only that, Television will start becoming a popular source of immersive experiences. The next-gen televisions will be equipped with high definition cameras, as well as AI technology to analyze a viewer’s emotions as they watch shows.
18. AR AI: Augmented reality content will be brought to smartphones
Augmented Reality first garnered worldwide attention with the release of Pokemon Go. Following which a large number of organizations invested in the development of AR-enabled smartphones in 2017. Most notable was Apple’s ARKit framework, which allowed developers to create augmented reality experiences for iPhone and iPad. Following which Google launched ARCore, to create augmented reality experiences at Android scale. Then came Snapchat, which released Lens Studio, a tool for creating customizable AR effects. The latest AR innovation came from Facebook, which launched AR Studio in open beta to bring AR into the everyday life of its users through the Facebook camera. For 2018, they are planning to develop 3D digital objects for people to place onto surfaces and interact within their physical space.
2018 will further allow us to get a taste of augmented reality content through beta products set in the context of our everyday lives. A recent report published by Digi-Capital suggests that mobile AR market will be worth an astonishing $108 billion by 2021. Following this report, more e-commerce websites will engage mobile users using some form of AR content seeking inspiration from the likes of the Ikea Place AR app. Apart from these, more focus would be on building apps and frameworks which consumes less battery life and have high mobile connectivity capability.
With this, we complete our list of our 18 in 18’ AI trends to watch. We would love to know which of our AI-driven prediction surprises you the most and the trends which you agree with. Please feel free to leave a comment below with your views.
Happy New Year!