In the 2019 year, AI will become just another sequential thread in the web of our lives - just like the Internet or electricity. Against this backdrop, the discussion on AI in the concept of a "black box" is no longer relevant: the world will place emphasis on building trust in artificial intelligence systems, since the benefits of its application are obvious both for business and for society.
10 predictions that people expect from the possibilities of artificial intelligence
- New intelligent systems will automate the task partly and without additional costs
Experts predict that in the 2019 year there will be many new intelligent tools in the form of complete applications and systems, through which it will be possible to automate the necessary business processes in an electoral manner. In addition, automation can be phased within one expanded pilot project, which will make the technology more accessible to medium and small businesses. This will help both increase the demand for business innovations and increase productivity among developers. In addition, according to McKinsey, only fewer 5% of professions in 2019 year can be objectively fully automated - others, on the contrary, will require human resource infusion and the ability of human employees to decide issues that machines are not able to solve so far, and and it is unlikely to study.
- For "universal" AI at enterprises there will be more analytic applications that will allow its functional operation.
Over the past few years, companies around the world have spent billions of billions on creating and debugging processes and infrastructure so that the so-called "universal" AI can perform the functions laid down with fewer errors. Resources are needed, for example, to unlock disparate data sources, to improve analytics for their most important analysis, to enhance personalization, forecasting and detection features, to detail monitoring of failures and abnormalities. It is anticipated that in 2019, developers will spend more time creating additional analytical applications that will send the "universal" AI that is already universally applied in the right direction - that is, to adapt it clearly where it is first needed. This will enable the business to operate the functionality of the technology to the full, avoiding financial losses. In addition, such developments will stimulate the development of in-depth training in the areas most in demand for it, since very often the "universal" AI, paradoxically, lacks "intelligence" to cover issues as they arise, rather than post fact. To do this, the system must have a deeper analysis of data and focus on the key needs of a particular process.
- UX / UI design will be critically demanding
Many modern solutions in the field of artificial intelligence go straight ahead with consumers, staff and experts in the subject area. These systems improve user productivity and in many cases allow them to perform tasks with incredible scale and precision. Proper design of the UX / UI not only simplifies these tasks, but also helps users trust the AI and more actively apply solutions at its base. In 2019 year, the relevant direction will become critically demanded and will be actively developed.
- More specialized hardware for perception systems, model training and data analytics will appear
The revival of deep learning began around the 2011 year to improve the unique systems for the reception and reproduction of language and computer vision. Today's hardware for scaling such options has gone crazy - Facebook alone makes trillions of forecasts for a day, and Google is capable of supporting their own capacities and even building them up. It is anticipated that in 2019 year there will be a wider choice of specialized hardware equipment, which will enhance the functional of perception systems, model training and data analytics. Numerous companies and startups, in particular, in China and the United States, have been working not only for the first year on hardware, focused on the construction and training of intelligent systems, both in the data center and on peripherals.
- Hybrid intelligence systems will be in the top priority
Deep machine learning requires enormous effort, learning and additional development to maximize the effectiveness and scale of its capabilities. Against this backdrop, hybrid intelligence systems, due to their "flexibility", can close the question more locally, which is very convenient. That is why in 2019 the year of development in methods of machine learning, which are not based on neural networks, will be extremely popular and will probably be at the peak of growing popularity.
- Machine training "split" into tools and inline into the most effective tools
ML development should take into account the importance of data, experiments and searches for models and systems, as well as their deployment and monitoring. Today, many companies are increasingly beginning to consider these options separately, rather than in a complex: they study the available intelligent tools that are needed for a particular process, as well as the ability to adapt them in different business conditions and areas of activity. It is anticipated that in the 2019 year, more information, developments and implementations based on partial opportunities and AI and ML tools will be introduced that will further simplify the implementation of many products and services. And the growth of investment in such studies and experiments will increase in geometric progression.
- Problems with data security will be greater
Despite new data protection methods, we still live in the era of machine-generated content (fake images, video, audio, text). At the moment, modern intelligent technologies focused on cybersecurity are capable of detecting, analyzing and often only partially eliminating unwanted or false information and protecting data. However, these tools are still imperfect when it comes to the ways and areas of their application. While tools for creating counterfeit content or breaking sensitive data are quickly being improved, companies around the world will be very worried about this. "Machine fraud" does not just refer to cars that deceive people. It also refers to other machines that deceive cars (bots) and people deceiving machines. Unpublished information dissemination methods will continue to be used to deceive content ranking systems and retail platforms, and methods of detecting and combating these phenomena will become more urgent and will need to be developed and work to evade data security breaches.
- The issue of data protection will be on the agenda of each company
Researchers and developers have been seriously addressing the issue of privacy, private equity, and ethics for the first year when it comes to the use of artificial intelligence, especially on a global scale. But, as deployment of AI systems, in critical analytic applications, the increased efficiency of automation should be accompanied by maximum accurate predictions and a genuine guarantee of security and reliability. The growth of machine fraud on online platforms, in particular, raises the question of data protection on the agenda for each company.
- Companies will have access to more data for deploying intelligent systems
Because many intelligent systems and models, including the deep learning that many enterprises rely on, require a large amount of data; the expected leaders in their use are large global companies or countries directly with the appropriate resource for technology scaling. But today, many services for creating labeled datasets are starting to use open-source machine learning tools to help their employees scaling and increasing their accuracy. And in certain areas, new tools such as generational adversarial networks (GANs) and simulation platforms can provide realistic synthetic data that can be used to teach machine-learning models at low cost. With new and safe privacy practices, organizations can use data that they themselves did not create. Consequently, small organizations will be able to compete using machine learning and AI.
- Global automation is not a trend but the 2019 trend of the year
This fact has confirmed more than one study: automation will continue to be one of the most active areas for the deployment of AI in 2019 year. And, according to the Harvard Business Review, 44% of global companies have said that they are already using AI to detect and prevent security invasion, 41% reported that they are already using AI to address the issue of human potential and technology interaction, and 34% who already use AI to improve production management through automation, as well as to evaluate and analyze internal business processes.