Forecasting AI development in 2019 Industry investment will increase penetration into all fields
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The application of artificial intelligence (AI) is ubiquitous, and this area has absorbed a lot of investment, and a large number of startup companies have emerged. Forbes visited the industry's big coffee development forecast for 2019 artificial intelligence. The common feature is that AI will continue to penetrate into various fields, including medical, financial, education and robotics, and investment will increase.
1. Some AI applications have been widely publicized, but in the short-term future AI will not completely subvert the current world. People have been developing autonomous vehicles, and some people are worried that the full application of artificial intelligence may take only 20 years to achieve, but the fact is that we still have a long way to go from real autonomous vehicles. As for the full application of artificial intelligence, this will only exist in science fiction movies in the short term. My prediction is that our expectations of artificial intelligence and its capabilities and the level of reality can be neutralized. In the next five years, society will look like this, but our daily work will undergo subtle and important changes, and we will become more and more efficient. AI robots will be able to answer questions and review customer service more effectively, the assistant will be more capable of completing the task, and the autopilot function will continue to improve, but they will not travel on the road on a large scale.
2. The adoption of artificial intelligence products will continue to expand into different vertical areas such as manufacturing, education, and retail in 2019. For example, in the healthcare arena, artificial intelligence-enhanced applications can reduce emergency waiting times and even use AI to detect and diagnose tumors to help doctors save time. As technology advances, applications move into a variety of vertical areas, while technology costs are reduced, and organizational and business products or results are improved, which are expected to accelerate the adoption of artificial intelligence. At Lenovo, we've used artificial intelligence in our supply chain and parts planning processes so that we can provide a first-class experience for customers who want to leverage artificial intelligence to transform their business.
3. In addition to the chat bots currently available on mobile applications and other healthcare IT platforms, patients can also talk on their own through a variety of omnichannel user interfaces. For conversational experiences like Alexa and Google Home, its consumer framework may add HIPAA privacy support. This way the robot can keep a conversation with the patient when the human doctor is inconvenient. In the care process, a customer-centric robotic medical assistant can replace the nurse to complete the call button, collect health history forms, and some tedious adjustments.
4. In 2019, the market focus will shift to edge execution analysis. The company saves time and money by processing and analyzing data at the edge rather than moving it back to the center, re-storing and then applying it. Use cases include anomaly detection (fraud), pattern recognition (predictive failure/maintenance), and persistent flow. Early applications include autonomous vehicles, oil and gas platforms and medical devices, and we will see further expansion of these technologies in 2019. The cost driver is that the semi-connected environment with relatively low cost of use can reduce bandwidth costs, while reducing the data sent to the cloud can also reduce the pressure on information storage.
5. The public will pay more and more attention to the issue of artificial intelligence ethics. 2018 is a year of public awareness, then 2019 will be a year of action. Not only are data ethicists and human rights advocates demanding fairness, accountability, and transparency. Consumers are already changing the way they use Facebook, or even completely removing their accounts, and this trend may spread to other social media, or other service platforms that use personal data. In the future, there will be more statements that standardize the creation and use of artificial intelligence, and the company has to adopt it. On the human right side, the public will oppose the government's use of biased artificial intelligence tools. More employees will demand increased influence and voice over what they create and will refuse to contribute to harmful automation tools.
6. Advanced analytics and artificial intelligence will continue to become more focused, built specifically for specific needs, and these features will increasingly be embedded in management tools. This highly anticipated feature will simplify IT operations, improve the robustness of infrastructure and applications, and reduce overall costs. With this trend, artificial intelligence and analytics will embed high availability and disaster recovery solutions, and cloud service offerings to improve service levels. The ability to quickly and automatically understand problems and diagnose problems in complex configurations will greatly improve the reliability and availability of cloud services.
7. With the continuous development of chat bots and artificial intelligence, the functions they can perform will increase in depth and breadth. What does this mean for the workforce, positive or negative? On the one hand, machine learning will help people screen large amounts of data and get the job done more efficiently. On the other hand, as people become more familiar with robot interactions, customer service and customer support positions will be phased out. This will begin more comprehensively in 2019 as more and more companies use artificial intelligence and chat bots to increase the productivity of existing employees or to phase out positions that can be accomplished with these technologies.
8. In industrial-strength artificial intelligence, many systems are debugged and evaluated based on data sets created and tagged by thousands (or more) of human evaluators. As the problem of artificial intelligence we solve is more complicated, the demand for a large number of high-quality manual judgments will increase, but there will be more time to make breakthroughs when using machine learning techniques to collect these judgments, and the cost-effectiveness is more obvious. At the same time, methods that use minimal or no tagged data (also known as unsupervised techniques) will reduce our reliance on large amounts of tagged data, enabling deep learning models to evolve on new and different types of issues.
9. Google Knowledge Atlas will usher in more changes, and the required technical NLP, graphical database and content analysis will make it easier for knowledge maps to write knowledge in various fields. Chat bots, guided process tools, and automation consultants are now available, and we will see more and more industries and sectors using these tools in the future, including healthcare, financial services, and supply chains.
10. AI has become mainstream through innovations in autonomous vehicles, smart speakers and facial recognition tools. The use of AI applications in logistics, manufacturing, healthcare, and cybersecurity is less obvious but equally influential. The unique thing about network security is that it is an important part of all other technologies. Whether we choose to live in a world of "smart" or "artificial intelligence," one thing is certain: if AI and deep learning do not enhance cybersecurity policies, they are more likely to be hacked. Artificial intelligence to strengthen cyber defense can block some cybercriminals.
11. Artificial intelligence is entering the commodity era. Consumers don't need to know how microwave technology works, because it's just a tool. With the influx of codeless, point-and-click tools, the use of AI has entered the same stage, and everyone uses AI as a practical tool regardless of the technical background. Therefore, in the next few years, most AI applications will be built by people with little or no AI training technology.
12. Robotic Process Automation (RPA) has been one of the hottest technology areas for the past two years. Because of simplicity and understanding, process automation means increased efficiency, the ability to free up resources, and focus on higher-value activities. But this technology has fundamental limitations. It is only effective for rote and repetitive processes, and there is no way to influence the workflow of unstructured content. More than 80% of data in most enterprises is such unstructured data. At the same time, artificial intelligence and machine learning are too esoteric and require more data science expertise to adopt, so the uncertainty is relatively large, and the return on investment is not necessarily ideal. In 2019, the company will seek a way to combine the benefits of these two technologies, and it is also a tool or technology that many experts call intelligent process automation.
13. For industries such as contact centers, many practical applications of artificial intelligence (AI) and machine learning (ML) have been exaggerated. For example, the practice of identifying specific patterns through images or data is not very effective. The company adds self-service session tools through the automation of smart applications to solve common problems and business processes that guide user flows, which in turn is better. Employees can focus on processes that cannot be effectively automated, and call centers can further optimize their operating models.
14. In 2019, the artificial intelligence toolkit will move to specific enterprise issues such as IT and human resources. So far, companies have been using AI toolkits to build custom applications to solve difficult problems. And now it is turning to artificial intelligence to solve common business problems.
15. In 2019, the first companies to adopt AI technology would want to get more value from their investments because they expect cloud applications to have more and richer built-in AI solutions in terms of functionality, user experience and accessibility, such as multiple Device applications, chat bots, and digital assistants. The company will invest in third-party data sources and intelligent data (such as dynamic signals and flexible classification of regular updates) to optimize output. As companies adopt machine learning, the trust, transparency, and interpretability issues of AI technology are gaining increasing attention.
16. Marketers have long discussed an ideal course of action. However, if no AI synthesizes large amounts of data in real time, the ideal solution is unlikely to be implemented. Artificial intelligence takes over the human task of a large data set, which means that 2019 may bring changes to the marketing community.
17. In 2019, artificial intelligence will become an important part of the marketing strategy. AI models in the areas of predictive analytics, sentiment analysis, and programmatic advertising will revolutionize all aspects of marketers' automated marketing channels, and they can develop highly targeted marketing (ABM) strategies. This requires investing in new technology, but it can also reduce the cost of custom acquisition by increasing marketing costs.
18. Artificial intelligence and machine learning will be new solutions to simplify operations. The IT skills gap will allow advanced companies to implement new and innovative solutions to automate complex operations. Machine learning and artificial intelligence will be key to new IT solutions, and smarter operations and modern IT solutions can help companies narrow their skills gap. Enterprise software companies will allow their respective vendors to integrate AI and ML into existing products, providing a more efficient operating model and achieving expectations.
19. Almost every IT department uses artificial intelligence to automate enterprise monitoring, reduce the manual work of IT staff, and strive to implement self-healing applications.
20. There are many startups in the robotics industry that are trying to capture a potentially large market share. However, in order to be successful, robotic start-up companies must take into account regulatory regulations from the outset of design, so that products comply with applicable safety regulations, or they will fail when they go public.