We are on the cusp of the biggest change the world has ever seen. You might think Brexit was disruptive, the war in Ukraine is disruptive, or covid was disruptive. AI (Artificial Intelligence) will bring more change than all of those put together.
For ten years now, companies like Google, Microsoft, Apple, Meta, Amazon, Tesla and IBM, have been developing software which learns how to improve, thereby giving the company a competitive advantage. Capitalism is funding AI, and no government has yet put a regulatory 'legal' framework around where this all will go.
The largest companies are also on a quest for quantum computing. When that happens commercially, whoever gets to a commercially viable quantum computer will have the biggest advantage for AI. One Quantum computer will be exponentially more powerful, millions of times more powerful than any other computer on the planet.
The nexus of the evolution of AI will be when AI is powered by one or more quantum computers.
AI is currently siloed, a fragmented web of systems owned by different companies, working independently of each other. Companies are giving AI learning tasks until they become more expert at a task than the people programming them.
For example, Google set up a robotic arm project which learned how to grasp and grab objects and identify what they were. They placed a bunch of children's toys in front of each one and then let those arms attempt to hold them. Weeks went by, tens of thousands of attempts before the first arm in the room picked up and identified a yellow tennis ball. Within days all the other arms were able to do that, and now the system can identify and sort all of the objects placed before it within seconds.
That doesn't sound like a groundbreaking endeavour, but what a child would take a year to learn, AI can take days. Mo Gawdat, a significant player in the development of AI at Google, has openly stated that their AI was operating at an IQ of 155, Einstein was 160. Mo predicts an I powered by a quantum computer will have an IQ of 1,600 within three years.
In another experiment using language, the AI software bot taught itself Persian within six months and was deemed to be more accurate in its language than any single human.
Why is this so potentially impacting?
Education.
Imagine a scenario where a student submits homework or a paper they never wrote. Chat GPT already allows anyone in society to ask a question to it and get an almost instant response. Schools, colleges and Universities will have to change the way they examine any written submitted work.
The whole area of education could be irrelevant, society deeming through personal choice that they should rely on a computer response rather than learn something themselves.
Institutions will likely increase their endowments and create new study subjects to propel AI forward itself, shifting millions of people's focus into the development of AI in the process.
Medicine.
In the UK, the NHS, which employs 1.4 million people, has pockets of call centres nationwide. They already use simple AI algorithms, which 111 call handlers use to route people to care. People already complain they no longer have free access to a physical meeting with a GP. Instead, they are 'processed' through computer code to prioritise their need.
Eventually, it's hoped that AI will diagnose much faster than a human could, will route people to care avoiding the common bureaucracy that people are frustrated by, and, of course, save billions of pounds for the government in the process.
AI could well benefit advances in cancer care, learning how best to treat people and even inventing new techniques for treatment. In a recent study on antibiotics, AI was used to come up with the best combination of drugs for effective treatment.
Personal Intellectual Property.
You can use an image or voice generator to create a picture of someone IE, create a picture of Carl Brettle rising a horse down Everest, which will be indistinguishable from a real photo. A model in the USA, created a chatbot, making it look like she would vocally respond to any sultry comment request for a fee. She made $70,000 in her first week of operation and never did any work beyond creating the platform to mimic her voice and respond.
Famous people can be copied, and who regulates that? Imagine Madonna 'the singer' being replicated to play an online concert she doesn't know about. Who would she sue?
Fake videos, songs or pictures can already be made of anyone. It's entertainment at the moment, but what happens when it becomes more sinister? Someone in school bullying someone else by generating a compromised picture of another completely innocent student.
Transport.
Tesla's AI system has been learning for several years. Tracking the driving habits of every car it has ever sold. Mapping every street those cars have driven on. Each year they advance, they glean more and more data. The aim is autonomous self-driving vehicles. AI is at the centre of that. How does the car drive in the rain, the sun, through fog? How close should the car be to the car in front, etc. etc?
Tesla currently has 2 million cars on the road, and it collects data every time they drive. Within its business plan, it aims to allow owners to 'rent out' their cars as self-driving taxis and charge a percentage of the fee. Taxi driving as an industry of 18 million drivers will become redundant, while one company takes a slice of every taxi fare globally each day.
In the home.
People already use, Alexa & Siri, software which improves itself through machine learning. Algorithms work out your shopping habits online and predictively put adverts in front of you based on your shopping patterns in the past.
We have robotic vacuum cleaners, and the ability to control, lights, heating and security with our voice or mobile phone.
Tesla has already announced their intention to build human-size robots for the home. Robots will have a level of intelligence to undertake the tasks we don't want to or can no longer do. We need to remember they will be stronger and faster at doing anything we have previously done. Play chess with you, and discuss any topic in the world with you as these machines will be connected to the internet.
The downside is, AI in a home like this will also bring constant surveillance, and no one is talking about what happens when they get hacked by an unscrupulous outside group.
The AI systems that provide all of this tech, will learn your every action in order to predictively 'help' you. The AI will know you better than anyone, as it will be there constantly, never forgetting anything you ever did.
Jobs.
From what I've already written, the jobs market will be affected. People who steer diagnosis, educationalists who could be replaced by AI teaching bots, taxi drivers etc. Goldman Sachs has estimated 50% of jobs could be lost in the market as AI takes full effect. That will mean a lot more poverty, the cost to the government to support that and inevitably retraining the workforce in - AI
Government and Elections.
If an agency or government tasks a powerful AI bot to promote or slander a candidate in an election, it will perform that task relentlessly. The Trump election campaign in 2016 poured millions of dollars into a simple AI system run by Cambridge Analytics to precisely target social media users with an individual message that might swing their vote. None of that was deemed illegal at the time, but in the aftermath, one party wants to go back to same-day counted paper ballots for voting, and the other party wants legislation to regulate how AI is used for election ads in social media. Both parties believe other governments were infiltrating the bias of election campaigns with AI.
Countries around the world will eventually regulate, but sadly that regulation will be in likely be confined to any country's particular borders. The best method of regulation would be a global agreement on the use of AI, and how it protects and serves humanity. This has been done before through things like the anti-proliferation agreement for Nuclear Weapons for example.
Conclusion
You may want to think about the implications of AI over the next few years. The kind of jobs your children may get, the impact of even more advertising and social media that is AI powered.
There is a healthy number of people who are already deciding to run simpler lives, to come off systems like social media altogether, to focus on real people, growing their own vegetables, being less reliant on big chain supermarkets and the like.
There will be benefits to AI, particularly in medicine, the issue is the companies driving its advance are profit-based and unregulated. When the first AI system becomes self-aware, then who knows what will happen next.
Definition dictionary.
Algorithm: A set of rules that a machine can follow to learn how to do a task.
Artificial intelligence: This refers to the general concept of machines acting in a way that simulates or mimics human intelligence. AI can have a variety of features, such as human-like communication or decision-making.
Artificial General Intelligence (AGI): this is when an AI system achieves
Autonomous: A machine is described as autonomous if it can perform its task or tasks without needing human intervention.
Backward chaining: A method where the model starts with the desired output and works in reverse to find data that might support it.
Bias: Assumptions made by a model that simplify the process of learning to do its assigned task. Most supervised machine learning models perform better with low bias, as these assumptions can negatively affect results.
Big data: Datasets that are too large or complex to be used by traditional data processing applications.
Chatbot: A chatbot is program that is designed to communicate with people through text or voice commands in a way that mimics human-to-human conversation.
ChatGPT: Chat (G)enerative (P)re-trained (T)transformer, has the ability to receive complex questions and almost instantly provide cohesive responses. IE you could ask this system to write a 5,000-word essay on Climate Change, with 50 points a person could practically do in the style Shakespeare writes. You would get a response in 10 seconds.
Cognitive computing: This is effectively another way to say artificial intelligence. It’s used by marketing teams at some companies to avoid the science fiction aura that sometimes surrounds AI.
Computational learning theory: A field within artificial intelligence that is primarily concerned with creating and analyzing machine learning algorithms.
Data mining: The process of analyzing datasets in order to discover new patterns that might improve the model.
Data science: Drawing from statistics, computer science and information science, this interdisciplinary field aims to use a variety of scientific methods, processes and systems to solve problems involving data.
Dataset: A collection of related data points, usually with a uniform order and tags.
Deep learning: A function of artificial intelligence that imitates the human brain by learning from the way data is structured, rather than from an algorithm that’s programmed to do one specific thing.
Forward chaining: A method in which a machine must work from a problem to find a potential solution. By analyzing a range of hypotheses, the AI must determine those that are relevant to the problem.
General AI: AI that could successfully do any intellectual task that can be done by any human being. This is sometimes referred to as strong AI, although they aren’t entirely equivalent terms.
Hyperparameter: Occasionally used interchangeably with parameter, although the terms have some subtle differences. Hyperparameters are values that affect the way your model learns. They are usually set manually outside the model.
Intent: Commonly used in training data for chatbots and other natural language processing tasks, this is a type of label that defines the purpose or goal of what is said. For example, the intent for the phrase “turn the volume down” could be “decrease volume”.
Linguistic annotation: Tagging a dataset of sentences with the subject of each sentence, ready for some form of analysis or assessment. Common uses for linguistically annotated data include sentiment analysis and natural language processing.
Machine intelligence: An umbrella term for various types of learning algorithms, including machine learning and deep learning.
Machine learning: This subset of AI is particularly focused on developing algorithms that will help machines to learn and change in response to new data, without the help of a human being.
Neural network: Also called a neural net, a neural network is a computer system designed to function like the human brain. Although researchers are still working on creating a machine model of the human brain, existing neural networks can perform many tasks involving speech, vision and board game strategy.
Natural language generation (NLG): This refers to the process by which a machine turns structured data into text or speech that humans can understand. Essentially, NLG is concerned with what a machine writes or says as the end part of the communication process.
Natural language processing (NLP): The umbrella term for any machine’s ability to perform conversational tasks, such as recognizing what is said to it, understanding the intended meaning and responding intelligibly.
Natural language understanding (NLU): As a subset of natural language processing, natural language understanding deals with helping machines to recognize the intended meaning of language — taking into account its subtle nuances and any grammatical errors.
Overfitting: An important AI term, overfitting is a symptom of machine learning training in which an algorithm is only able to work on or identify specific examples present in the training data. A working model should be able to use the general trends behind the data to work on new examples.
Quantum Computing: Quantum uses computer science, physics, and mathematics, utilising quantum mechanics to solve complex problems faster than on classical computers. Quantum computers are able to solve certain types of problems faster than classical computers by taking advantage of quantum mechanical effects, such as superposition and quantum interference. Some applications where quantum computers can provide such a speed boost include machine learning (ML), optimization, and simulation of physical systems. Eventual use cases could be portfolio optimization in finance or the simulation of chemical systems, solving problems that are currently impossible for even the most powerful supercomputers on the market.
Pattern recognition: The distinction between pattern recognition and machine learning is often blurry, but this field is basically concerned with finding trends and patterns in data.
Predictive analytics: By combining data mining and machine learning, this type of analytics is built to forecast what will happen within a given timeframe based on historical data and trends.
Reinforcement learning: A method of teaching AI that sets a goal without specific metrics, encouraging the model to test different scenarios rather than find a single answer. Based on human feedback, the model can then manipulate the next scenario to get better results.
Sentiment analysis: The process of identifying and categorizing opinions in a piece of text, often with the goal of determining the writer’s attitude towards something.
Strong AI: This field of research is focused on developing AI that is equal to the human mind when it comes to ability. General AI is a similar term often used interchangeably.
Supervised learning: This is a type of machine learning where structured datasets, with inputs and labels, are used to train and develop an algorithm.
Training data: This refers to all of the data used during the process of training a machine learning algorithm, as well as the specific dataset used for training rather than testing.
Transfer learning: This method of learning involves spending time teaching a machine to do a related task, then allowing it to return to its original work with improved accuracy. One potential example of this is taking a model that analyzes sentiment in product reviews and asking it to analyze tweets for a week.
Turing test: Named after Alan Turing, famed mathematician, computer scientist and logician, this tests a machine’s ability to pass for a human, particularly in the fields of language and behavior. After being graded by a human, the machine passes if its output is indistinguishable from that of human participant’s.
Unsupervised learning: This is a form of training where the algorithm is asked to make inferences from datasets that don’t contain labels. These inferences are what help it to learn.
Variance: The amount that the intended function of a machine learning model changes while it’s being trained. Despite being flexible, models with high variance are prone to overfitting and low predictive accuracy because they are reliant on their training data.
Variation: Also called queries or utterances, these work in tandem with intents for natural language processing. The variation is what a person might say to achieve a certain purpose or goal. For example, if the intent is “pay by credit card,” the variation might be “I’d like to pay by card, please.”
Weak AI: Also called narrow AI, this is a model that has a set range of skills and focuses on one particular set of tasks. Most AI currently in use is weak AI, unable to learn or perform tasks outside of its specialist skill set.
Sources
Taxis
Teslas sold
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