AI leaders who make AI the foundation of their business strategy will gain an advantage over competitors. Such companies will leverage AI for business outcomes while industrializing operations and training employees on its use.
Artificial intelligence (AI) can be seen everywhere around us – from personalizing social media feeds to identifying and deleting fake news – but what exactly is its function?
What is AI?
Artificial Intelligence, or AI, refers to software or hardware devices designed to mimic human cognitive functions. AI systems use data and information to process and learn, allowing them to accomplish tasks such as understanding speech, playing games or recognizing patterns. They may be programmed or learn through trial-and-error – such as repeatedly playing a video game until they learn its rules and how to win – although human supervision often plays an instrumental role in this learning process by reinforcing good decisions while discouraging bad ones.
Artificial intelligence’s most prevalent form is machine learning, in which algorithms apply to newly ingested data in search of patterns and predictions. This process can be greatly expedited by access to large volumes of labeled training data at affordable storage costs as well as processing capabilities for both structured and unstructured information.
There are four primary categories of artificial intelligence (AI), from task-specific reactive machines currently in widespread use to intelligent systems that do not yet exist. Type 1 involves reactive machines without memory that perform specific tasks – for instance, Deep Blue was an example of this type.
AI can be employed in numerous applications, from predicting health outcomes and financial fraud analysis to providing recommendations on social media. While AI may improve enterprise performance and productivity while making sense of massive amounts of data and providing significant business benefits, organizations that utilise it must remain mindful of its potential to create biases or discriminatory outcomes – whether intentional or unintentional – and address any ethical concerns in an ethical manner by creating robust policies to mitigate them.
Type 1: Reactive Machines
Reactive Machines are the simplest form of AI, characterized by simple responses to immediate input. Reactive machines don’t store information or form internal concepts of their world – instead they react instantly in response to what’s in front of them at that moment – creating what could be termed as ‘garbage in, garbage out’ machines.
Spam filters and Netflix recommendation engines are reactive machines; so are the chess-playing supercomputers that defeated Garry Kasparov in 1990, as well as today’s self-driving cars. But they don’t make for very intelligent machines: They cannot learn from past experience and won’t improve when presented with similar situations again.
One incredibly useful characteristic of AI systems is speed. When we require instantaneous responses – for instance, airbags that deploy upon car accidents, or the ability of computers to instantly process data and produce results without user intervention – AI provides invaluable assistance.
Not to worry! Reactive Machines aren’t the only intelligent algorithms available today – there are others, like generative AI or natural language processing technology which convert voice commands into text or commands, that offer solutions. Generative AI models may present risks of misuse; for instance, they can easily be altered to produce false or deceptive information or instruct against unethical or illegal acts. As these AI models use real-world data for training purposes, they may be exposed to gender, racial, and other biases prevalent in society. Furthermore, technical bugs or security vulnerabilities similar to software code errors could pose threats that require special consideration when training these systems on ethics and legal guidelines. Therefore, organizations utilizing this type of AI must take particular caution in training these systems in accordance with ethical or legal norms.
Type 2: Limited Memory
Contrary to reactive machines, limited memory AI has the unique capability of remembering information learned in past experiences and applying it in present decisions. Furthermore, this type of AI can monitor objects or situations over time in order to recognize patterns and anticipate future outcomes.
Limited memory AI’s primary application is natural language processing, or the recognition, interpretation and production of human speech. To do this effectively, limited memory AI utilizes deep learning, which uses multiple layers of interconnected nodes to form hierarchical representations of data that enables systems to detect subtle correlations or dependencies missed by traditional data modeling techniques.
Real-time applications are best served by using AI that prioritizes relevant information over irrelevant ones, limiting resources such as memory and computing power needed for storage purposes, while increasing privacy and security by only storing essential files – decreasing risk that sensitive personal information might be compromised in this manner.
As technology develops, limited memory AI will become ever-more essential in various industries. For instance, it can detect patterns in financial data or scientific research to make predictions for the future. Unfortunately, we’re still far away from creating AI capable of understanding emotions or picking up environmental cues – known as theory of mind – which would enable it to understand other people’s intentions and respond appropriately; reaching this level is considered the “holy grail” in AI but doesn’t yet exist.
Type 3: Theory of Mind
Artificial Intelligence powers many of the software and gadgets we rely on daily, from internet searches and cybersecurity measures to voice recognition for personal assistants and picture unlocking in cellphones, AI is everywhere we look. Behind the scenes it also helps customize social media feeds, recognize fake news stories and assist us with tasks such as driving cars or shopping online.
There are four distinct categories of artificial intelligence (AI), from task-specific systems widely employed today and reaching forward into space-time with sentient systems that do not yet exist. Weak AI refers to computer programs that perform specific tasks but lack general cognitive abilities; an example would be IBM’s Deep Blue computer which defeated human chess champion Garry Kasparov back in the 90s.
AI technology comes in three basic varieties. One form is known as limited memory AI systems that learn from their past experiences – this type is used in self-driving cars for instance. Another form is theory of mind AI systems. Theory of mind involves understanding that other people have different beliefs, thoughts, and emotions than yourself – this complex mental skill remains unknown to scientists today and most research on theory of mind studies involve toddlers or infants as the subjects. One test commonly used is known as false-belief task in which children are presented with scenarios in which characters hold false beliefs regarding something they encounter or experience.
Strong AI or artificial general intelligence (AGI) is the fourth and final type of artificial intelligence. AGI refers to programming that replicates human cognitive capabilities across multiple domains; similar to what humans are able to accomplish. AGI represents the natural progression from current AI technology, potentially leading to superintelligent machines capable of surpassing human intelligence in every aspect.
Type 4: Self-Awareness
An AI that is self-aware can assess its own performance and adjust accordingly, going beyond task-specific machines currently widely employed. A famous example is IBM’s Deep Blue computer which famously defeated Garry Kasparov at chess back in 1990s.
Alongside AI technologies already in widespread use, newer systems are also being created. This includes tools that use generative AI technology to write and edit code as well as software allowing robots to comprehend natural language.
These kinds of tools will eventually become capable of working alongside human colleagues to perform more complex tasks, while currently they’re used in healthcare, retail and the financial industries to perform data analysis, predictive maintenance and security services. Furthermore, they’re particularly helpful for automating repetitive processes which don’t require much creativity or human judgement.
As more companies adopt AI, their teams must receive adequate training. Training options range from certification programs like Simplilearn to in-person classes taught by experienced instructors and should help prepare your team for AI’s emergence by using the best technologies and methodologies that deliver exceptional results.
Artificial intelligence may generate much excitement among organizations today, yet most are only beginning to dabble with it. Without fully adopting AI solutions, organizations risk falling behind competitors who do. When beginning AI implementation efforts, start small by targeting activities with the highest impact on cost and productivity such as accounting or IT functions to maximize ROI while mitigating risks.