Evolution of AI: Past, Present, Future

Christina Aguis
Feb 2 · 7 min read
Photo by rawpixel on Unsplash

The History of Artificial Intelligence

Although the concept of artificial intelligence has been around for centuries it wasn’t until the 1950’s where the true possibility of it was explored. A generation of scientists, mathematicians and philosophers all had the concept of AI but it wasn’t until one British Polymath, Alan Turing, suggested that if humans use available information, as well as reason, to solve problems and make decisions — then why can’t machines do the same thing? Although Turing outlined machines and how to test their intelligence in his paper Computing Machinery and Intelligence in 1950 — his findings did not advance.

The main halt in growth was the problem of computers. Before any more growth could happen they needed to change fundamentally — computers could execute commands, but they could not store them. Funding was also an issue up until 1974.

By 1974 computers flourished. They were now faster, more affordable and able to store more information. Early demonstrations such as Allen Newell and Herbert Simon’s General Problem Solver and Joseph Weizenbaum’s ELIZA, which was funded by Research and Development Corporation (RAND), showed promise toward the goals of problem-solving and the interpretations of spoken language in machines, and yet there was still a long way to go before machines could think abstractly, self-recognize and achieve natural language processing.

In the 1980s AI research fired back up with an expansion of funds and algorithmic tools. John Hopfield and David Rumelhart popularized “deep learning” techniques which allowed computers to learn using experience. On the other hand, Edward Feigenbaum introduced expert systems which mimicked decision making processes of a human expert. But it was not until the 2000’s that many of the landmark goals were achieved and AI thrived despite lack of government funds and public attention.

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Today’s AI Research

In today’s day, AI research is constant and continues to grow. Over the last five years AI research has grown by 12.9% annually worldwide, according to technology writer Alice Bonasio.

Within the next four years China is predicted to become the biggest global source of artificial intelligence, taking over the United States’ second lead in 2004 — and it is quickly closing in on Europe’s number one spot.

Europe is the largest and most diverse region with high levels on international collaboration within the field of artificial intelligence research. After China and the United States, India is the third largest country in terms of AI research output.

When it comes to specifics, there are seven distinct research areas with limitations on AI ethics research.

· Search and Optimization

· Fuzzy Systems

· Natural Language Processing and Knowledge Representation

· Computer Vision

· Machine Learning and Probabilistic Reasoning

· Planning and Decision Making

· Neural Networks

Neural networks, machine learning, and probabilistic reasoning and computer vision show the largest volume of research growth.

Present Effects of AI

There is so much that artificial intelligence is being used for and so much more potential that it is hard to picture our future without help it — especially when it comes to business.

From workflow management tools to trend predictions and even the way brands purchase ads, machine learning technologies are driving increases in productivity like never before.

Artificial Intelligence can collect and organize large amounts of information to make insights and guesses that are beyond the human capabilities of manual processing. It also increases organizational efficiencies yet reduces the likelihood of a mistake and detected irregular patterns, like spam and fraud, to warn business in real time about a suspicious activity — among many other things. AI is said to reduce costs in many ways — for example, “training” machines to handle incoming customer support calls and replacing many jobs in that way. It’s also known that if your business doesn’t use AI it’s probably falling behind competitively.

AI has become so important and advanced that a Japanese Venture Capital firm made history by being the first company to nominate an AI Board Member for its capabilities to predict market trends faster than a human.

Artificial intelligence will be and is becoming a commonplace in every aspect of life — like the future of self-driving cars, more accurate weather predictions, or earlier health diagnosis’, just to name a few.

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A Smarter Future

It has been said that we are on the cusp of the Fourth Industrial Revolution — a revolution that is completely different than the previous three. From steam and water power, electricity and assembly lines, and computerization to now challenging the ideas about what it means to be human.

According to Forbes, the Fourth Industrial Revolution “describes the exponential changes to the way we live, work and relate to one another due to the adoption of cyber-physical systems, Internet of Things and the Internet of Systems.”

Smarter technologies in our factories and workplaces and connected machines that will interact, visualize the entire production chain and make decisions autonomously is just a couple of the ways that the Industrial Revolution will cause advancements in business. One of the greatest promises that the Fourth Industrial Revolution brings is the potential to improve the quality of life for the world’s population and raise income levels. Our workplaces and organizations are becoming “smarter” and more efficient as machines, humans are starting to work together, and we use connected devices to enhance our supply chains and warehouses.

According to Gigabit Magazine, there are seven stages that will create a smarter world with AI:

1. Rule-Based Systems — domestic applications and RPA software that surrounds us everywhere, every day.

2. Context Awareness and Retention — algorithms that build up a body of information that is used and updated by machines. For example, chatbots and roboadvisors.

3. Domain Specific Expertise — machines that can develop expertise in a specific field that extends beyond the capability of humans because of all the informational access they can quickly get to, to reach a decision.

4. Reasoning Machines — these algorithms have a “theory of mind,” some ability to attribute mental states to themselves and others. They have a sense of beliefs, intentions, knowledge, and are aware of how their own logic works. Hence, they have the capacity to reason, negotiate, and interact with humans and other machines.

5. Self Aware Systems — the goal for those working in the AI field is to create and develop systems with human-like intelligence. There is no such evidence of that today but some say that there will be in as little as five years while others believe we may never achieve that level of intelligence.

6. Artificial Superintelligence — developing AI algorithms that are capable of outperforming the smartest of humans in every single domain.

7. Singularity and Transcendence — a development path enabled by ASI that could lead to a massive expansion of human capability, where one day we might be sufficiently augmented and enhanced such that humans could connect their brains to each other and to a future successor of the current internet.

Envisioning AI in the Next 20 Years

2020–2025

· Between 70% and 90% of all initial customer interactions are likely to be conducted or managed by AI

· Product development in a range of sectors from fashion items and consumer goods to manufacturing equipment could increasingly be undertaken and tested by AI

· Individuals will be able to define and design the personalised products and services they require in sectors ranging from travel through to banking, savings, and insurance

· The technology is likely to be deployed across all government agencies and legal systems — with only the most complex cases requiring a human judge and full court proceedings

· Autonomous vehicles will start appearing in many cities across the world

· Our intelligent assistants could now be managing large parts of our lives from travel planning through to compiling the information we need prior to a meeting.

2026–2035

· Globally approved, smart crypto tokens may be accepted alongside fiat currencies as we edge towards a single global medium of exchange

· Artificial intelligence is likely to have penetrated every commercial sector

· The evolution of AI could see the emergence of a wide range of fully automated DAO businesses including banks, travel agents, and insurance companies

· Scientific breakthroughs could enable us to develop artificial animal and ecosystem intelligence

· The emergence of self-aware and self-replicating software systems and robots

· There is a reasonable possibility of achieving Artificial General Intelligence

· There is a small chance of creating Artificial SuperIntelligence

· The singularity remains an unlikely possibility in this timeframe.

Sources

https://medium.com/edtech-trends/report-global-ai-research-a1582f2f88c5

https://www.wired.com/insights/2015/01/the-evolution-of-artificial-intelligence/

https://www.gigabitmagazine.com/ai/evolution-ai-seven-stages-leading-smarter-world

https://www.forbes.com/sites/forbeslacouncil/2018/11/15/how-ai-empowers-the-evolution-of-the-internet/#1cfcd5103256

http://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/

https://www.livescience.com/47544-history-of-a-i-artificial-intelligence-infographic.html

https://www.forbes.com/sites/williamfalcon/2018/11/30/this-is-the-future-of-ai-according-to-23-world-leading-ai-experts/#3da122062f27

https://www.forbes.com/sites/forbestechcouncil/2018/12/11/14-predictions-about-the-future-of-ai-and-vr/#3e7da0c66466

http://www.pewinternet.org/2018/12/10/artificial-intelligence-and-the-future-of-humans/


Data Driven Investor

from confusion to clarity, not insanity

Christina Aguis

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Data Driven Investor

from confusion to clarity, not insanity