Thinking with Technology: The Rise of AI in the Modern World

For years, humanity has envisioned and dreamed of a future with autonomous and flying vehicles, a clean and safe environment, and a healthy, extended life.

Today, more than sixty years after John McCarthy coined the term “artificial intelligence” to describe the science and engineering of making machines intelligent, and after surviving two so-called “AI winters” (in the 1970s and 1980s when progress was slow, and the commercial impact was disappointing), the recent advancements in the field suggest that this future is on its way.

 

Recent years have revealed an increasing number of cases where AI beats humans in narrow domains such as playing the game Go. Although Artificial General Intelligence (or AGI, ‘thinking machines‘ that are comparable and may even be better than the human mind) still belongs to the distant future, researchers in the field believe that machines are slowly coming closer to human levels at performing ‘simple’ tasks, such as understanding naturally spoken language. In fact, one of the most common applications of AI today is speech recognition. Personal virtual assistants such as Alexa, Siri, Cortana and Google Assistant can understand speech and respond to it accordingly. The biggest breakthrough in speech recognition thus far has come from IBM, who has managed to reduce the error rate in conversational speech recognition to 5.5%. The human error rate is at 5.1%.

Aside from speech recognition, current applications of AI include predictive technologies found in self-driving cars and search engines. Companies such as Netflix and Pandora are also using AI to improve their products and services.

Why AI?

AI is widely used today. It’s no longer just a figment of our imagination, a concept relegated to science fiction. Technology is continuously working towards the creation of machines that can think and perform tasks without human intervention.

Still, some people wonder: Why do we need AI?

Most companies invest in the development of AI to make the production of goods and services more efficient and cost-effective. Governments are interested in AI because it can be used in weaponry. Some believe AI is the next step in our evolution as a species.

Andrew Ng, the former chief scientist of Chinese tech company Baidu, provided a more concrete answer about why AI is being developed. According to him, AI can transform many industries. It can perform tasks that are currently done by humans but at much greater speed. For example, certain machines can detect suspicious behavior in security videos, thus improving safety. It can place targeted ads so that businesses can efficiently reach their desired market. It can even provide company to the lonely.

Meanwhile, Eric Schmidt, executive chairman of Google’s parent company Alphabet, has said that more developed AI will be able to solve some of the world’s biggest problems including climate change and food shortage. To most developers, this is the main reason why they continuously seek advancement of the technology: so that AGI can provide solutions to problems humans can’t seem to resolve.

 

Core Paradigms

An AI-future is inevitable. This prompts the question: What are the paradigms of applying AI in the coming years?

In the distant future, an AI-human hybrid may be possible or even necessary, so we can increase our mental skills and master scientific challenges. This hybrid may also extend to combining our bodies with artificial ones, allowing us to improve our physical abilities.

Machines have long been good at understanding our functional needs; at answering “what?” but never “why?” However, recent advancements in the field have allowed AI to read a user’s emotional needs as well. Although these machines are yet to understand emotion, businesses are now using a technology that can read their customers’ needs and how a certain item affects a customer’s decision-making process. We have finally reached a point where humans and machines can build engaging relationships, allowing businesses to provide more personalized services through AI. This is the starting point for the three core paradigms that will shape the applications of AI technology in the coming years:

1) Conversational AI

The internet has enabled us to connect, share and engage without time, location or other physical constraints. And now, there’s a craze about bots that’s being driven by humankind’s current favorite communication technology: messaging apps. Conversational interfaces allow us to engage chatbots, although the technology is yet to have social intelligence. It still lacks the broad understanding of each engagement’s context to create a meaningful and valuable interaction with the user.

However, Gartner predicts that conversational AI, when used properly with visual solutions and UX, will supersede today’s cloud and mobile-first paradigm by 2021.

Although I can see this happening (and also agree that most enterprise apps will make use of conversational AI-first solutions in the coming 4 to 5 years), we’ve got to regard them as a form of business process automation, which users can more efficiently and easily use than the static, pre-programmed workflows we are using today. These conversational AI-first tools will act autonomously within the provided framework to achieve a certain set of goals and react to its environment. It will, therefore, act on a user journey’s individual context.

Moreover, conversational AI is currently primarily used facing towards users. But, looking at it from an angle of business processes automation, I expect a much bigger use in bot-to-bot communication in the next 2 to 3 years. This type of bot will result in a true personal assistant.

Bot-to-bot communication will be the most used form of interaction involving bots. It will extract the real value from these systems, providing access to information sets that couldn’t be provided by user-facing interactions, and even less by competitive services at scale in the past.

2) Mass-Individualization

After apps, products and services have been developed to cover as many user needs as possible. Therefore, they have never been able to achieve mass adoption in areas where such tools have to work in complex environments with various stakeholders and many variables.

Having said this, we are now approaching a time where every digital offering will be highly personalized. Mass individualization will take over today’s mass standardization. While the latter involves businesses trying to scale repetitive workflows and processes to attract a large group—and sometimes even the most homogenous user group, mass-individualization will enable entrepreneurs and product managers to build their offerings around each user’s personal engagement context, which will include factors such as their behavior, attitude, goals, and needs.

The change to mass-individualization, which we’re now seeing in content and e-commerce, will push the digitization of industries due to its ability to transfer complex processes and engagement models that usually require a large amount of service. This will also drive the adoption of what are currently highly costly human-only services, such as accounting and legal advice. It will allow other industries to serve a much larger customer base.

As a result, mass individualization will change the way products are made. Besides a constant customer feedback loop, new offerings will be created automatically based on an individual’s current and future needs. Web applications will be able to serve and produce specific items on the fly, via bot-to-bot communication and real-time customer engagement. This trend will lead us to a world where machines are deeply integrated into our everyday lives.

3) AI-Enabled Convergence

By definition, technological convergence is the tendency that different technological systems will evolve towards performing similar tasks. New technologies take over to perform the same task but in a more advanced manner.

AI-enabled convergence means that AI-based technologies are embedded in all new systems which provide smart, context-aware and pro-active products and services that can engage with humans in a more natural and smarter way. These systems can either be based purely on software applications or on robotics that engage with us physically.

AI will be used together with enabling technologies such as Blockchain, a distributed but controlled network of billions of systems connecting and interacting with each other, and IoT, which allows systems to collect, send and receive information about their environment, condition, and other values. It will also be used with other technologies such as VR/AR, 3D printing, autonomous robotics, renewable energy sources, and advanced genomics such as Next Generation Sequencing (NGS), to transform industries and our everyday lives, as well as our socioeconomic existence.

Conclusion

These three core paradigms are going to shape the way we make, use and engage with machines in the next few years. Speaking of which, more advancements are expected soon. For instance, dueling neural networks will allow machines to produce realistic synthetic data by giving machines the ability to 1) generate new data from a training set, and 2) distinguish which data is real or fake. Positive reinforcement, which helped AI win a Go game against one of the best players of all time, will make data centers more efficient. Lastly, language learning will be more effective and is expected to result in a lower error rate.

When it comes to the distant future, these three core paradigms will guide us in handling our relationships with the technology. As we continuously seek the development of AI, we hope that it will ultimately provide us with the tools that make everyday life easier.