Artificial Intelligence (AI) is a broad field of computer science focused on creating systems capable of performing tasks that typically require human intelligence. These tasks include reasoning, learning, problem-solving, perception, and language understanding. AI can be categorized into several types based on its capabilities, functions, and application domains.

Types of Artificial Intelligence

1. Narrow AI (Weak AI)

Definition: Narrow AI, also known as Weak AI, refers to artificial intelligence systems that are specialized and focused on performing a specific task or a set of closely related tasks.

Characteristics:

• Task-Specific: Designed to handle specific functions such as image recognition, language translation, or playing a game.
• Limited Scope: Operates within a predefined range and lacks the ability to generalize beyond its programmed tasks.
• No Self-Awareness: Cannot understand or reason outside its specific application.

Examples:

• Voice Assistants: Siri, Alexa, Google Assistant. They can perform tasks like setting reminders or answering questions but cannot engage in conversations outside their designed capabilities.
• Recommendation Systems: Used by platforms like Netflix or Amazon to suggest products or movies based on user preferences and behavior.
• Autonomous Vehicles: Systems like Tesla’s Autopilot use machine learning to navigate roads but are limited to driving tasks and cannot engage in other activities.

2. General AI (Strong AI)

Definition: General AI, or Strong AI, refers to an advanced form of AI that has the capability to understand, learn, and apply intelligence across a wide range of tasks, much like a human being. This is still a theoretical concept and has not yet been realized.

Characteristics:

• Broad Competence: Capable of performing any intellectual task that a human can.
• Contextual Understanding: Can understand and reason about diverse subjects and contexts.
• Adaptability: Can transfer knowledge from one domain to another and learn new tasks with minimal additional input.

Examples: As of now, there are no existing examples of General AI. It remains a subject of research and speculation, with ongoing debates about its potential development and implications.

3. Superintelligent AI

Definition: Superintelligent AI refers to a hypothetical AI that surpasses human intelligence across all fields, including creativity, general wisdom, and problem-solving. This concept is often discussed in the context of long-term future scenarios.

Characteristics:

• Superior Capability: Possesses cognitive abilities that are far beyond the best human minds.
• Potential Risks: Raises concerns about control, ethical implications, and the potential impact on society and humanity.
• Speculative Nature: Discussions around Superintelligent AI are largely theoretical and focus on its potential development and consequences.

Examples: No real-world examples exist. Superintelligent AI is often explored in science fiction and theoretical discussions about the future of AI.

4. Reactive Machines

Definition: Reactive machines are basic AI systems that operate purely on the present input without the ability to form memories or use past experiences.

Characteristics:

• Immediate Response: Reacts to specific inputs with predefined responses.
• No Learning: Does not learn from past interactions or experiences.
• Simple Design: Often simpler in design and implementation compared to more advanced AI systems.

Examples:

• IBM’s Deep Blue: A chess-playing AI that defeated grandmaster Garry Kasparov. It used predefined strategies and calculations without learning from previous games.
• Basic Chatbots: Simple bots that provide scripted responses based on keywords or phrases.

5. Limited Memory AI

Definition: Limited memory AI systems have the ability to use past experiences to improve their performance and make better decisions over time. They can retain and learn from data but only within a specific context.

Characteristics:

• Experience-Based Learning: Uses historical data to inform current decision-making.
• Contextual Memory: Can remember and use past interactions within a specific domain.
• Adaptive: Capable of improving performance as more data becomes available.

Examples:

• Self-Driving Cars: Utilize past driving data to make decisions about navigation and obstacle avoidance.
• Fraud Detection Systems: Learn from historical transaction data to identify patterns indicative of fraudulent behavior.

6. Theory of Mind AI

Definition: Theory of Mind AI aims to develop systems that can understand and simulate human emotions, beliefs, intentions, and mental states. This type of AI is still in the research phase.

Characteristics:

• Emotional Understanding: Able to recognize and respond to human emotions and intentions.
• Advanced Interaction: Facilitates more natural and intuitive interactions between humans and machines.
• Research Focus: Involves ongoing research to achieve a deeper level of human-like understanding.

Examples: No existing examples; the development of Theory of Mind AI is a goal for future AI advancements.

7. Self-Aware AI

Definition: Self-Aware AI refers to AI that has a sense of self and consciousness, including awareness of its own internal states and the ability to reflect on its actions and existence.

Characteristics:

• Self-Recognition: Has an awareness of its own state and existence.
• Reflective: Capable of introspection and understanding its role and impact.
• Ethical and Philosophical Implications: Raises profound questions about the nature of consciousness and the rights of AI.

Examples: No current examples; self-aware AI remains a theoretical concept and is the subject of philosophical and ethical discussions.

Each of these types represents a different level of complexity and capability in AI. The field is rapidly evolving, and future advancements may lead to new forms of AI or refined classifications.

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Future multi-domain battles will require swarms of dynamically coupled, coordinated heterogeneous mobile platforms to overmatch enemy capabilities and threats.

The US Army is looking to swarming technology to be able to execute time-consuming or dangerous tasks. Swarming is a method of operations where multiple autonomous systems act as a cohesive unit by actively coordinating their actions. 

Swarms of unmanned aerial and ground vehicles will now be able to optimally accomplish various missions while minimizing performance uncertainty, thanks to a development by US Army researchers..

The researchers developed a reinforcement learning approach that optimizes guidance policies for the swarming vehicles in real-time. The move will enhance warfighters’ tactical situational awareness, allowing the U.S. Army to dominate in a contested environment.

Reinforcement learning provides a way to optimally control uncertain agents to achieve multi-objective goals when the precise model for the agent is unavailable; however, the existing reinforcement learning schemes can only be applied in a centralized manner, which requires pooling the state information of the entire swarm at a central learner, according to eurekalert.org. This drastically increases the computational complexity and communication requirements, resulting in unreasonable learning time..

The Army funded this effort through the Director’s Research Award for External Collaborative Initiative, a laboratory program to stimulate and support new and innovative research in collaboration with external partners.

The main goal is to develop a theoretical foundation for data-driven optimal control for large-scale swarm networks, where control actions will be taken based on low-dimensional measurement data instead of dynamic models.

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AI is fast progressing in every field like healthcare, manufacturing, law, finance, retail, real estate, accountancy, digital marketing. Every field is being computational and becoming more advanced with some remarkable capabilities emerging these past years.  Artificial Intelligence is reshaping the economy and career landscape with a wide range of diverse patterns from autonomous systems, chatbots, document classification to advanced predictive analytics solutions. Albeit most of the problems faced are in terms of driving down costs, removing friction, speeding processes up, and improving efficiency that can be mitigated as the machines are becoming smarter day by day.

Use of  knowledge graphs in professional services

Google introduced a newer type of database called the knowledge graph in the year 2012. This type of database are compatible and multiple databases can be interrogated simultaneously. Knowledge graphs link triplets of information’s subject, object, and the relationship between them. This flexible approach allows us to get precise answers to rich and complex questions. 

Problems with knowledge graphs are that they are time-consuming, and expensive to populate. This problem can be mitigated by the rapid improvement in AI called natural language processing, which enables knowledge graphs to be populated automatically. The use of knowledge graphs in professional services like accountancy and law would greatly improve and speed up the work as data retrieval is automated. 

Reinforcement learning: the next big thing of AI

“Reinforcement learning” is a classic behavioral phenomenon known as psychology literature in the year 1950s, said Dr. Matt Johnson who is the professor of psychology at Hult Business School and the author of the Blindsight: the (mostly hidden ways marketing reshapes our brains).

According to this theory, the agent comes in contact with the surrounding environment and observes a particular activity which then responds to the results depending upon the surrounding.

AI-powered characters will adapt to produce elaborate storylines, and consumers will no longer be confined to fixed dialogues and rigid interaction between non-player markets.

The AI doctor might try medications almost randomly to see what effect they have, and overtime should learn the patterns. and understanding of which medications work best in which situation. 

Dark matter: Turns AI understand images like humans

The scientists at Massachetrus Institute of Technology and the University of California, Los Angeles, argue that the key to making AI systems is about visual data like human AI to address “dark matter” of computer vision, the things that are not visible in pixels.

AI in content marketing

AI is a  dynamic game-changer for digital marketing from 24/7 chatbots, analyzing data and trends, custom feeds to generating content. AI recommends content topics that align with the trending topic. AI recommends SEO strategies for more relevant and valuable content. AI is creating custom, personalized content for buying users at each stage of customer experience.

The chatbots provide real-life customer experience so they don’t have to fill the generic form. This provides users quick and easy answers to their questions for their immediate purchase. Users feel they are having a real conversation with humans while it’s a robot.

Conclusion

AI is becoming affordable and accessible for any software developer. The tech giants are currently competing to dominate the field of artificial intelligence. AI is a necessity in the year 2020 that will witness a growth in facial recognition, computer vision, autonomous vehicles, automatic text generation, cloud, AI-enabled chips, and privacy and policy.

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Artificial intelligence, for all its benefits, needs human oversight. Government reports, and experts all over the world have stressed the importance of keeping a human decision-maker in the loop when using AI. 

“Human agency and oversight” is the first key requirement laid out in the EU Commission’s white paper on the regulation of AI published earlier this month, and establishing oversight of “the whole AI process” is also a recommendation from the UK’s Committee on standards in public life. Only this week, the Metropolitan police chief Cressida Dick reiterated her commitment to having human workers always making final decisions in policing, rather than letting new technologies overrule officers’ authority.  

In the US, the Pentagon released guidelines last year on the ethical use of AI for military purposes. Among the chief recommendations, the document also features the need for an “appropriate” level of human judgement whenever deploying an autonomous system.

It’s all well and good to recommend that humans consistently monitor the decisions made by AI systems, especially if those decisions impact decisive fields like warfare or policing. But in reality, how good are humans at catching the flaws of those systems? 

Not good enough, according to Hannah Fry, associate professor in the mathematics of cities at University College London. Speaking at a conference organised by tech company Fractal in London, Fry explained that having a human overseeing an AI system does not entirely solve the problem – because it doesn’t do much to overcome innate human flaws. According to Fry, and we place excessive trust in AI systems with consequences that can sometimes be dramatic.

“If there is one thing you can say for sure, it’s that you cannot trust people,” said Fry. “As humans, we are lazy and we take cognitive shortcuts. Misplacing our trust in machines is a mistake that all of us are capable of doing.”

Case in point: a few years ago, three Japanese tourists found themselves driving into the Pacific Ocean off the coast of Australia while trying to reach North Stradbroke island, because their GPS system had failed to account for the nine miles of water lying between the island and the mainland.

The anecdote might be entertaining; but it turns out that people are a lot more like those Japanese tourists than we’d think them to be, said Fry. In this case, the main damage caused by the tourists’ over-reliance on GPS technology was the loss of the their rented Hyundai Getz; but our trust in technology can come at a much greater cost, for example when we get to relying on self-driving cars. 

In driving, she explained, humans are bad at paying attention, at being aware of their surroundings and at performing under pressure. And yet, she noted, the idea behind driverless cars is that the human monitor should step in at the last possible moment and operate at peak performance, at the moment where it matters the most. 

Having a human overrule the automated decision-maker in the car? “That’s not something that’s going to happen,” warned Fry.

That is not to say that algorithms should not be deployed altogether. Quite the opposite: Fry herself is a self-professed defender of artificial intelligence, and of the huge benefits it could bring to fields such as healthcare. But there is one simple rule that should apply to all AI systems, according to the mathematician: we should only use algorithms as long as we can trust humans to overrule them when necessary.

In a research paper published in 2018, think tank Pew surveyed almost 1,000 technology experts to gather their insight on the future of humans in the age of artificial intelligence. One of the main take-aways was a similar concern to that of Fry: that people’s growing dependence on algorithms would eventually erode their ability to think for themselves.

The solution, for Fry, lies in adopting a “human-centric” approach when developing new technologies; in other words, an approach that accounts for human flaws. The mathematician advocated for a “partnership” between humans and machines that could combine the best of both – while also ensuring that there is always space for humans to question the algorithm’s results.

One particular field where such a partnership could have promising results is healthcare. When diagnosing cancer, for instance, doctors face the imperative of being both sensitive, to not miss any sign of a tumor; as well as specific, to avoid the unnecessary over-flagging of suspicious tissue. 

While humans are “rubbish” at sensitivity, Fry said that algorithms are “ultra-sensitive”; and on the other hand, she described specificity as our “human superpower”. Combining both sets of skills, she concluded, could have tremendous results for healthcare. 

“This is the kind of future I am hoping for,” she said. One where we acknowledge, when deploying new technology, that it’s not only machines that have flaws; but that humans do too.

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The Digital Single strategy of the European Commission sets out to “open up digital opportunities for people and business and enhance Europe’s position as a world leader in the digital economy.”(1) This article will briefly examine a part of that by looking at the work of the Robotics and Artificial Intelligence (Unit A.1).

We know that the Unit sets out to push forward the development of a competitive industry in robotics and artificial intelligence (AI) throughout Europe. Certainly, this includes industrial and service robots plus the growing field of autonomous systems from drones and driverless vehicles to computing and cognitive vision.

Also, the Unit encourages the wide uptake and best use of robotics and AI in all societal and industrial fields. The Unit takes responsibility for the European Commission’s participation in the contractual PPP on Robotics and for the implementation and development of the relevant strategic industrial agenda.

As well as managing RD&I projects within Horizon 2020, the UNIT keeps up-to-date with legal and ethical issues in the field of robots and autonomous systems, for example, aspects related to the impact of automation and robotics on jobs and work environment, as well as liability and safety.

Currently, Juha Heikkilä is Head of Unit at Robotics and Artificial Intelligence (Unit A.1) (2). Juha joined the European Commission in 1998 and today works in their (3) Directorate-General for Communications Networks, Content and Technology (DG CONNECT). This is the department “responsible to develop a digital single market to generate smart, sustainable and inclusive growth in Europe.” (4)

In July 2019, we find out that the European Commission launched a call to develop a vibrant European Network of AI Excellence Centres. Under the Horizon 2020 Work Programme 2018-2020, proposals for this can be submitted up to 13th November 2019. Europe has important potential to lead technological advancements in AI, with a strong research infrastructure and world-class community of scientists at their disposal. It is, therefore, crucial that the crème de la crème research teams in Europe collaborate to combat significant technological and scientific challenges.

The Commission is looking ahead at a long-term effort to unify the European AI community and make the region an AI powerhouse. To achieve this, they believe that two actions are needed:

1. Research and Innovation Action to mobilise the best researchers into networks of excellence centres to reach a critical mass on important AI topics.
2. Coordination and Support Action to enable exchange between the selected projects, as well as other relevant initiatives.

These aforementioned actions should create synergies with the industrial sector and, “foster an ecosystem of R&D resources, expertise and infrastructure (in areas such as HPC, robotics equipment, IoT infrastructure).” (5)

In other news, a significant piece of robotics news from the European Commission came in late 2018 when they awarded €66,000,000 to robotics projects that will help digitise companies throughout the European Union (EU). As part of the Digitising European Industry Call of Horizon 2020, the EU’s research and innovation programme, one coordination support action and four projects have been awarded. We read more about this on the European Commission’s website.

“They will all help small and medium-sized enterprises (SMEs) adopt new technologies in the robotics and artificial intelligence area. Nearly half of the money dedicated to these Digital Innovation Hubs (DIHs) projects will be dispatched to local companies by involving them in mini-projects or experiments.”

The four awarded projects are:

1. DIH^2: A network of 26 DIHs to reach no less than 170 DIHs.
2. DIH-HERO: A broad pan-European network of DIHs specialising in healthcare robotics will be established.
3. TRINITY: The aim here is to create a network of multidisciplinary DIHs consisting of research centres, university groups companies that cover a wide array of topics.
4. RIMA: This sets out to establish a network of 13 DIHs on robotics to facilitate the uptake of maintenance and inspection and maintenance technologies. (6)

The topics briefly discussed are just a few examples of the excellent work of the Robotics and Artificial Intelligence (Unit A.1) within the European Commission and we look forward to future developments in this most exciting area of work. It’s a really important aspect of the Digital Single Market but it’s not the only part of it.

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