A Brief History of AI: Where Are We Going – 2020++


(Maybe you’re just interested in what we have to say about the future of AI, but if you came to this episode from the previous two episodes, congratulations, you’ve walked a linear path from this brief narrative about the history of the area. In case you are coming straight here, I invite you to read the previous two episodes first, so you can find out about our entire series on the history of AI.)

Niels Bohr was an important Danish physicist who lived at the beginning of the last century and contributed significantly to the understanding of the atomic structure of matter and quantum physics. He has a famous phrase that I chose to start this episode: “Making predictions is very difficult, especially if they are about the future”. Therefore, I do not want to venture here to predict what the future of AI will be, but to deliver what is promised in the title, we are going to discuss trends and priorities in AI for companies and governments. For this, I will take two perspectives as references: the AI initiatives and policies of various governments; and the panorama of the areas of AI in the companies.

As of 2019, virtually all major world economies have established (or see establishing) National AI Initiatives (or Policies). Most of these proposals can be found on the website of the Organization for Economic Co-operation and Development (OECD) (https://www.oecd.org/going-digital/ai/initiatives-worldwide/), of the European Commission (https://ec.europa.eu/knowledge4policy/ai-watch/national-strategies-artificial-intelligence_en) or from the Future of Life website (https://futureoflife.org/ national-international-ai-strategies/) and I will bring a brief perspective here to share.

From the perspective of public policies, the report brought by the European Commission, available at the link above, identifies five priority areas: 1) human capital; 2) interface between the laboratory and the market; 3) connections (networking); 4) infrastructure; and 5) regulation. Policies involving human capital focus on formal, informal, labor market and training involving AI skills, from primary education to continuing education programs. In the laboratory-to-market context, priority will be given to supporting research and development activities, innovation and testing of AI-based technologies. The axis called networking proposes the promotion of collaboration, international attractiveness, dissemination and adoption of AI. Regarding infrastructure, emphasis will be placed on data (access, use, sharing, protection and analysis) and digital and telecommunication technologies, including the provision of large-scale computing infrastructures and the development of network technologies such as 5G. Finally, in the regulatory axis, AI policies emphasize the ethical (standardization and principles), legal and standardization aspects of AI. It is clear that governments are attentive to the expansion and relevance of the area, and should act in order to instruct (train), enable, promote and regulate Artificial Intelligence within their countries, aiming not only to remain competitive, but also to achieve differentials in in relation to other nations that are not structured for this. The very existence of national initiatives and policies shows the seriousness and relevance with which the area has been treated in various countries.

In the technological field, we will divide the trends in the area into infrastructure, applications and platforms. A challenge and focus of attention in infrastructure is the ability to address the Vs of Big Data (data in large volume, velocity and variety). Here we adopt the broad meaning of the word data, that is, everything that serves as input to AI we consider data, including structured, semi- and unstructured data, such as texts, audio, images and videos. Therefore, the first challenges and trends in the area are associated with governance, integration, storage, monitoring, generation, labeling, transformation and data analysis. Parallel and distributed processing, cloud and streaming solutions (streaming data or content) also make up this infrastructure list. Within the Information Technology (IT) area, a concept that has been expanding is that of AIOps, which refers to the use of AI to optimize IT operations, improving its management and automating problem solving.

About AI applications, well, there seems to be no limits, there are so many that it’s even difficult to list, but I’ll leave some areas here as a reference. AI has been very useful in people management, marketing, advertising and publicity, sales, user experience (customer), productivity, legal, logistics and operations, regulation ( compliance), security, education, retail, real estate, financial sector., insurance, health, agriculture and industry. The ultimate goal of AI applications is almost invariably the automation of systems, processes, and services, leaving humans with tasks that involve creating skills and physically interfacing with people. Repetitive tasks that can be done without much complexity are being left to specific AI applications.

Our third and final axis of AI technology trends involves platforms, that is, computing environments that allow the development of one or more specific solutions for more than one customer or end user. For example, a startup may have a specific solution that uses AI to manage contracts and processes, while another may have a platform that allows the construction of various legaltech solutions, such as data compilation and jurimetrics, document management, recovery and analysis of norms and legislation, etc. AI platforms aim to integrate diverse technologies into a single solution, facilitating, scaling up, reducing adoption costs and popularizing AI in the market. In addition to the specific solutions already listed above, AI and Big Data platforms are certainly consolidating as a trend in the market. And there are platform specializations too, we can find BI (business intelligence) platforms, data visualization, conversational and natural language processing, machine learning, Big Data, computer vision, robotics, autonomous navigation and others. Artificial intelligence is not a hype and will not go through a third winter, it is here to stay, it has already shown its importance and every day it occupies a greater space in the academy and in the productive sectors. It is up to us to understand this revolution and prepare for it. For now, we have discussed a lot the so-called Industry 4.0 or Fourth Industrial Revolution, but the moment we live in is one of changes. Industry 4.0 is associated with the transformation of the industry and its processes using smarter and more automated technologies such as the Internet of Things (IoT), AI, additive and subtractive manufacturing, cloud computing, virtual and augmented reality, autonomous robotics, Big Data and security. cybernetics. At the end of this industry 4.0 consolidation process, we will have a new world, with new ways of working and interacting with people and machines. Without a doubt, we are privileged to witness this revolution!

Compartilhe com sua rede