18 striking AI Trends to watch in 2018 – Part 1mengjiao.fu
Artificial Intelligence is the talk of the town. It has evolved past merely being a buzzword in 2016, to be used in a more practical manner in 2017. As 2018 rolls out, we will gradually notice AI transitioning into a necessity. We have prepared a detailed report, on what we can expect from AI in the upcoming year. So sit back, relax, and enjoy the ride through the future. (Don’t forget to wear your VR headgear! )
Here are 18 things that will happen in 2018 that are either AI driven or driving AI:
- 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
- Rise in immersive media experiences enabled by AI.
- A large number of organizations will use Digital Twin.
1. General AI: AGI may start gaining traction in research. AlphaZero is only the beginning.
2017 saw Google’s AlphaGo Zero (and later AlphaZero) beat human players at Go, Chess, and other games. In addition to this, computers are now able to recognize images, understand speech, drive cars, and diagnose diseases better with time.
AGI is an advancement of AI which deals with bringing machine intelligence as close to humans as possible. So, machines can possibly do any intellectual task that a human can! The success of AlphaGo covered one of the crucial aspects of AGI systems—the ability to learn continually, avoiding catastrophic forgetting. However, there is a lot more to achieving human-level general intelligence than the ability to learn continually. For instance, AI systems of today can draw on skills it learned on one game to play another. But they lack the ability to generalize the learned skill. Unlike humans, these systems do not seek solutions from previous experiences. An AI system cannot ponder and reflect on a new task, analyze its capabilities, and work out how best to apply them.
In 2018, we expect to see advanced research in the areas of deep reinforcement learning, meta-learning, transfer learning, evolutionary algorithms and other areas that aid in developing AGI systems. Detailed aspects of these ideas are highlighted in later points. We can indeed say, Artificial General Intelligence is inching closer than ever before and 2018 is expected to cover major ground in that direction.
2. Enterprise AI: Machine Learning adoption in enterprises will see rapid growth.
2017 saw a rise in cloud offerings by major tech players, such as the Amazon Sagemaker, Microsoft Azure Cloud, Google Cloud Platform, allowing business professionals and innovators to transfer labor-intensive research and analysis to the cloud. Cloud is a $130 billion industry as of now, and it is projected to grow. Statista carried out a survey to present the level of AI adoption among businesses worldwide, as of 2017. Almost 80% of the participants had incorporated some or other form of AI into their organizations or planned to do so in the future.
According to a report from Deloitte, medium and large enterprises are set to double their usage of machine learning by the end of 2018. Apart from these, 2018 will see better data visualization techniques, powered by machine learning, which is a critical aspect of every business. Artificial intelligence is going to automate the cycle of report generation and KPI analysis, and also, bring in deeper analysis of consumer behavior. Also with abundant Big data sources coming into the picture, BI tools powered by AI will emerge, which can harness the raw computing power of voluminous big data for data models to become streamlined and efficient.
3. Transformative AI: We will turn to AI enabled solutions to solve mission-critical problems.
2018 will see the involvement of AI in more and more mission-critical problems that can have world-changing consequences: read enabling genetic engineering, solving the energy crisis, space exploration, slowing climate change, smart cities, reducing starvation through precision farming, elder care etc.
Recently NASA revealed the discovery of a new exoplanet, using data crunched from Machine learning and AI. With this recent reveal, more AI techniques would be used for space exploration and to find other exoplanets. We will also see the real-world deployment of AI applications. So it will not be only about academic research, but also about industry readiness.
2018 could very well be the year when AI becomes real for medicine. According to Mark Michalski, executive director, Massachusetts General Hospital and Brigham and Women’s Center for Clinical Data Science, “By the end of next year, a large number of leading healthcare systems are predicted to have adopted some form of AI within their diagnostic groups.” We would also see the rise of robot assistants, such as virtual nurses, diagnostic apps in smartphones, and real clinical robots that can monitor patients, take care of the elderly, alert doctors, and send notifications in case of emergency.
More research will be done on how AI enabled technology can help in difficult to diagnose areas in health care like mental health, the onset of hereditary diseases among others. Facebook’s attempt at detection of potential suicidal messages using AI is a sign of things to come in this direction. As we explore AI enabled solutions to solve problems that have a serious impact on individuals and societies at large, considering the ethical and moral implications of such solutions will become central to developing them, let alone hard to ignore.
4. Safe AI: Safety, Ethics, and Transparency in AI applications will become integral to conversations on AI adoption and app design.
The rise of machine learning capabilities has also given rise to forms of bias, stereotyping and unfair determination in such systems. 2017 saw some high profile news stories about gender bias, object recognition datasets like MS COCO, to racial disparities in education AI systems.
At NIPS 2017, Kate Crawford talked about bias in machine learning systems which resonated greatly with the community and became pivotal to starting conversations and thinking by other influencers on how to address the problems raised. DeepMind also launched a new unit, the DeepMind Ethics & Society, to help technologists put ethics into practice, and to help society anticipate and direct the impact of AI for the benefit of all. Independent bodies like IEEE also pushed for standards in it’s ethically aligned design paper.
As news about the bro culture in Silicon Valley and the lack of diversity in the tech sector continued to stay in the news all of 2017, it hit closer home as the year came to an end, when Kristian Lum, Lead Statistician at HRDAG, described her experiences with harassment as a graduate student at prominent stat conferences. This has had a butterfly effect of sorts with many more women coming forward to raise the issue in the ML/AI community. They talked about the formation of a stronger code of conduct by boards of key conferences such as NIPS among others. Eric Horvitz, a Microsoft research director, called Lum’s post a “powerful and important report.” Jeff Dean, head of Google’s Brain AI unit applauded Lum for having the courage to speak about this behavior. Other key influencers from the ML and statisticians community also spoke in support of Lum and added their views on how to tackle the problem.
While the road to recovery is long and machines with moral intelligence may be decades away, 2018 is expected to start that journey in the right direction by including safety, ethics, and transparency in AI/ML systems. Instead of just thinking about ML contributing to decision making in say hiring or criminal justice, data scientists would begin to think of the potential role of ML in the harmful representation of human identity. These policies will not only be included in the development of larger AI ecosystems but also in national and international debates in politics, businesses, and education.
5. Ubiquitous AI: AI will start redefining life as we know it, and we may not even know it happened.
Artificial Intelligence will gradually integrate into our everyday lives. We will see it in our everyday decisions like what kind of food we eat, the entertainment we consume, the clothes we wear, etc.
Artificially intelligent systems will get better at complex tasks that humans still take for granted, like walking around a room and over objects. We’re going to see more and more products that contain some form of AI enter our lives. AI enabled stuff will become more common and available. We will also start seeing it in the background for life-altering decisions we make such as what to learn, where to work, whom to love, who our friends are, whom should we vote for, where should we invest, and where should we live among other things.
6. Embedded AI: Mobile AI means a radically different way of interacting with the world.
There is no denying that AI is the power source behind the next generation of smartphones. A large number of organizations are enabling the use of AI in smartphones, whether in the form of deep learning chips, or inbuilt software with AI capabilities. The mobile AI will be a combination of on-device AI and cloud AI. Intelligent phones will have end-to-end capabilities that support coordinated development of chips, devices, and the cloud. The release of iPhone X’s FaceID—which uses a neural network chip to construct a mathematical model of the user’s face— and self-driving cars are only the beginning. As 2018 rolls out we will see vast applications on smartphones and other mobile devices which will run deep neural networks to enable AI.
AI going mobile is not just limited to the embedding of neural chips in smartphones. The next generation of mobile networks 5G will soon greet the world. 2018 is going to be a year of closer collaborations and increasing partnerships between telecom service providers, handset makers, chip markers and AI tech enablers/researchers. The Baidu-Huawei partnership—to build an open AI mobile ecosystem, consisting of devices, technology, internet services, and content—is an example of many steps in this direction. We will also see edge computing rapidly becoming a key part of the Industrial Internet of Things (IIoT) to accelerate digital transformation. In combination with cloud computing, other forms of network architectures such as fog and mist would also gain major traction.
All of the above will lead to a large-scale implementation of cognitive IoT, which combines traditional IoT implementations with cognitive computing. It will make sensors capable of diagnosing and adapting to their environment without the need for human intervention. Also bringing in the ability to combine multiple data streams that can identify patterns. This means we will be a lot closer to seeing smart cities in action.
7. Data-sparse AI: Research into data efficient learning methods will intensify
2017 saw highly scalable solutions for problems in object detection and recognition, machine translation, text-to-speech, recommender systems, and information retrieval. The second conference on Machine Translation happened in September 2017. The 11th ACM Conference on Recommender Systems in August 2017 witnessed a series of papers presentations, featured keynotes, invited talks, tutorials, and workshops in the field of recommendation system. Google launched the Tacotron 2 for generating human-like speech from text.
However, most of these researches and systems attain state-of-the-art performance only when trained with large amounts of data. With GDPR and other data regulatory frameworks coming into play, 2018 is expected to witness machine learning systems which can learn efficiently maintaining performance, but in less time and with less data. A data-efficient learning system allows learning in complex domains without requiring large quantities of data. For this, there would be developments in the field of semi-supervised learning techniques, where we can use generative models to better guide the training of discriminative models. More research would happen in the area of transfer learning (reuse generalize knowledge across domains), active learning, one-shot learning, Bayesian optimization as well as other non-parametric methods. In addition, researchers and organizations will exploit bootstrapping and data augmentation techniques for efficient reuse of available data. Other key trends propelling data efficient learning research are growing in-device/edge computing, advancements in robotics, AGI research, and energy optimization of data centers, among others.
8. Conversational AI: AI personal assistants will continue to get smarter
AI-powered virtual assistants are expected to skyrocket in 2018. 2017 was filled to the brim with new releases. Amazon brought out the Echo Look and the Echo Show. Google made its personal assistant more personal by allowing linking of six accounts to the Google Assistant built into the Home via the Home app. Bank of America unveiled Erica, it’s AI-enabled digital assistant.
As 2018 rolls out, AI personal assistants will find its way into an increasing number of homes and consumer gadgets. These include increased availability of AI assistants in our smartphones and smart speakers with built-in support for platforms such as Amazon’s Alexa and Google Assistant.
With the beginning of the new year, we can see personal assistants integrating into our daily routines. Developers will build voice support into a host of appliances and gadgets by using various voice assistant platforms.
More importantly, developers in 2018 will try their hands on conversational technology which will include emotional sensitivity (affective computing) as well as machine translational technology (the ability to communicate seamlessly between languages). Personal assistants would be able to recognize speech patterns, for instance, of those indicative of wanting help. AI bots may also be utilized for psychiatric counseling or providing support for isolated people. And it’s all set to begin with the AI assistant summit in San Francisco scheduled on 25 – 26 January 2018. It will witness talks by world’s leading innovators in advances in AI Assistants and artificial intelligence.
9. AI Hardware: Race to conquer the AI optimized hardware market will heat up further
Top tech companies (read Google, IBM, Intel, Nvidia) are investing heavily in the development of AI/ML optimized hardware. Research and Markets have predicted the global AI chip market will have a growth rate of about 54% between 2017 and 2021. 2018 will see further hardware designs intended to greatly accelerate the next generation of applications and run AI computational jobs.
With the beginning of 2018 chip makers will battle it out to determine who creates the hardware that artificial intelligence lives on. Not only that, there would be a rise in the development of new AI products, both for hardware and software platforms that run deep learning programs and algorithms. Also, chips which move away from the traditional one-size-fits-all approach to application-based AI hardware will grow in popularity. 2018 would see hardware which does not only store data, but also transform it into usable information. The trend for AI will head in the direction of task-optimized hardware.
2018 may also see hardware organizations move to software domains and vice-versa. Nvidia, most famous for their Volta GPUs have come up with NVIDIA DGX-1, a software for AI research, designed to streamline the deep learning workflow. More such transitions are expected at the highly anticipated CES 2018.
[dropcap]P[/dropcap]hew, that was a lot of writing! But I hope you found it just as interesting to read as I found writing it. However, we are not done yet. And here is part 2 of our 18 AI trends in ‘18.