The term artificial intelligence has suddenly become a buzzword, due to how it has thrived at the very centre of major sectors of the world, including corporate and industrial affairs. An AI is an automated program that operates with occurring data, is carefully selective of events and matters, and what triggers them, and presents an almost accurate prediction of possible outcomes. Artificial intelligence systems almost always exploit human details and behaviour, natural causes and effects, compiles them into a prescribed algorithm, and present outcomes based on related phenomenons.
AIs have become seemingly “trustworthy” because of their programmed affiliations with human learning (deep learning) and written codes (machine learning) that cause them to easily identify a similar chain of case scenarios and suggest answers informed by observations gotten from those scenarios. Such suggests the idea of a linear equation that classifies related events/phenomenons to relay information about a prior related query. Input(available data)—>Analysis (Data manipulation)—>Output(possible outcomes) It has become a revolution and earned people’s confidence across all sectors because of the large quantum of stored data that it boasts of, and can feed out at a fast speed (automated pace).
History of AI
In 1956, John McCarthy coined the word “artificial intelligence”, and organized the first artificial intelligence conference in that same year. As AI revolution progressed, the first general mobile robot, Shaky, was created in 1969. It was able to carry out productive workloads as it had the ability to receive instructions. In 1997, the supercomputer “Deep Blue” was invented, and it defeated the great chess world player. In 2002, the first commercially robotic vacuum cleaner was invented. Seven years later, the world witnessed further developments such as speech recognition, robotic process automation (RPA), smart homes, etc. In 2020, the LinearFold AI was released by Baidu, for medical personnel and scientists who were preparing vaccines at the onset of the coronavirus (COVID-19) pandemic. The machine could detect the ribonucleic acid (RNA) of the virus in 27 seconds, which was considered faster than the other available methods.
Forms of AI
While creators of AI systems and applications may have gone amok with their apps, AI apps can only be classified under two categories;
- Weak AIs
- Strong AIs
Weak AIs function within the confines of a specific limit. They only have a specific function, and can only perform that single operation. AI systems like calculators, thermometers, compasses, security lock systems, etc. only have the ability to perform a specific operation, and cannot operate beyond that. Strong AIs can embrace multiple tasks, just like humans. Their intuition is programmed to solve complex problems. Like human behaviour, they can grasp, comprehend, reason, and even evaluate problem-related situations. Ideally, they are built to make recommendations in the face of choices. An example of such strong AI is Elon Musk’s Optimus robot, OpenAI’s ChatGPT etc.
The demarcation between machine learning and deep learning
Machine learning is a set of instructions created from observed data, meant for the computer to follow, in order to make predictions and recommendations. It is writing out specific instructions (algorithms) informed by previous case scenarios, in a language (programming) that the computer can understand, so as to present an informed outcome.
Machine learning is characterized of:
1. Data identification: in a bid to make the computer understand the instructions that are fed into it, it is made to sort through scores and scores of data, in order for it to become familiar with them.
The computer is made to “read” through a series of codes, in order to identify a variety of operations that it is commanded to solve. This task is a crucial integral part of machine learning because specific features of code operations are deliberately fed into the computer in order for it to easily comply with solving given tasks.
2. Supervised and unsupervised learning: for supervised learning, the computer is fed with labeled data- identifiable data- that it analysis according to categorized details. While unsupervised learning allows to the computer sort through unlabeled/unidentified data and identify similar patterns in features and properties for itself.
3. Broad data applicability: machine learning can sort through different variety of existing data. It accepts numerical data, text, image, and voice, and even suggests options for a system to work on.
Deep learning affiliates itself with artificial neural networks influenced by the human brain and it is characterized of:
1. Automatic data extraction: Deep learning instantly sorts through available data and identifies the relevant bits of it to utilize.
2. Deep neural network: Deep learning exploits a neural network employing multifaceted interconnected nodes (neurons) that can analyse complex hierarchical forms of data.
3. High performance: Deep learning can take up advanced tasks and integrate complex operations such as computer vision, natural language adaptation, and speech recognition, making it superior to the machine learning approach.
How AIs work
The operation is almost cyclical- available data, data analysis, observable outcome, and data-inferred prediction- are all tailored mechanisms that define artificial intelligence.
For an AI to work, it has to formulate its own program, its own set of operable instructions, that it can read even in its automated state, in order to give an informed outcome. It is in its own right that AI can predict the most likely outcome, but only after it has identified relevant data (machine learning + deep learning)
Data is anything manipulable, that is, a variable, that is capable of creating a cause-and-effect scenario, and further making grounds for probable outcomes. That means, a single result from a cause-and-effect situation, can further produce several other outcomes.
But AIs aren’t just independent automated program stuff that magically works on its own. It solely relies on both machine learning and deep learning for it to comprehend the sets of tasks it is given to do.
Machine learning is like a series of zeros and ones, or their possible combinations, or patterns- written codes- that the computer easily interpreted as commands. Every command poses as a logical operation that the computer has to follow
Examples of AI
Numerous artificial intelligence abounds in our present world, and they include:
1.Chat boxes
Chat bots have become commonplace simply because of the digital utilities they afford users. They are referred to as internet companions. Before now, chat bots were popularly relied on to respond to inquiries such as Frequently Asked Questions (FAQ), procedures on how to use a product, directions to a geographical location, and even technical advice. But the continuous exploitation of chatbots has led to further utilization of the program. Recent developments like OpenAI’s ChatGPT, a large language model (GBT 3), have been frequently besieged by all and sundry for their interactive responses to any request made to it about any subject. Artificial intelligence is a text-to-text presentation mode, that relays categorized information about anything inquired, from tips on how to behave on a first date, to the arithmetic formula required to solve an equation, to the best approach on how to win a legal case, and conditions that can happen while in space. The whole process is automated, requiring the user to only text in his inquiry, and he’d receive a prompt answer.
2. Google Duplex and Hold
Google Duplex is a rather social AI partner that can represent people in executing their schedules. Created in 2018, the model has a human voice and can identify contexts and situations
Automatic Speech Recognition
Google Duplex can book an appointment at the spa, set and communicate your flight schedule, and set up a meeting with your respective clients. Meanwhile, Hold for me helps to interface between you and another commercial worker. For instance, it could briefly hold off a discussion between you and another person on the other side of the line, in the event of an interruption, and simply inform you when either of you is ready to return to the call.
3. Smart Compose, Quick reply, and Grammar Check
Smart compose helps to predict the next text you are about to write, from your preceeding text. It simply understands word contexts through already organised systematic word arrangement and adds and completes your sentences for you.
This is common while composing email drafts and texts, to make for a faster write-up and instant content delivery.
Quick reply simply suggests instant possible responses to give a person who has sent you a message, especially on social media. This is common during private chats, and messages pop up on your notifications, and you are immediately suggested some reply options. It certainly makes online conversations quick and done with ease.
Apart from Grammarly, Grammar Check on Google Docs help content writers, journalists, speech writers, etc. to write more error-free sentences in their content.
It simply detects wrong spellings and poor word constructions and suggests appropriate word texts and phrases.
4. Microsoft’s Bing
Following its commercial release, Microsoft’s Bing AI serves over 100 million users who consult it for a vast degree of searches.
Since the tech company improved the search engine, just like ChatGPT, it gives prompt answers to any inquiry made to it. Moreso is the chatbot interface it affords users to interact with, making information search relatively easy and fast.
5. Google lens and OCR
Image detection and its minute properties have become possible due to the manufacture of Google’s AI lens tool.
The AI optical tool can just about view the details of an image, using its lens. Not only does it identify the appearance of the image, it can suggest secondary details of the image such as the environment/place the image was taken, the original context for which it was taken, and other events where the image has been used.
This is only possible because of the amount of photo data that Google has amassed. Every image has a distinct detail and the lens easily reads these characteristics, howbeit, with the help of machine language.
Moreover, Google Lens can do Optical Cognitive Recognition (OCR), that is, detect text data on an image just like Tesseract or TensorFlow.
6. Social media feeds
The type of media items and contents that appear on your feeds or the “For you” option, are done based on your personal interaction on social media. This is done using complex algorithms.
For instance, you tend to see more cryptocurrency news feeds and information, than any other social media content simply because you engage more often in that aspect online. You “click” more, “like”, “comment”, “quote”, and “share” than other topics.
So, you may have a different user experience from another user, in terms of content interaction, because of your personal preferences on social media. But this is carefully done by sampling algorithms effected by artificial intelligence.
7. Digital assistants
Many digital assistants such as Google Assistant, Alexa, Siri, and Bixby, contribute entirely to engaging users with the components that smartphones offer to them, causing them to use these services effortlessly.
These digital assistants have been trained with certain utilitarian functions, from simple tasks such as replying to questions that require “yes” or “no” answers, to giving directions, to offering pieces of advice.
These unseen AI models can do such complex tasks because they have been trained in both machine and deep learning language techniques. They are built using computer language such as machine codes and they are made to master human behavior (deep learning).
8. AI image generators
Image creation has now moved beyond evoking pictures by using pencil and colours to form lines and aesthetics, but by reason of digital exploitation, a multitude of images now abound. It’s now very easy to create images by means of text-to-image prompts using AI image-generation applications such as Midjourney.
Applications such as Midjourney have sampled a stock of photos and only require the user to exert his imagination in order for the image to be created. He can do this by just issuing text commands of the description of the desired image, and the AI just generates it.
It is noteworthy to know that what comes out as output is dependent on what the user has given as a description of the image.
9. Surveillance Cameras
The prospect of capturing and storing moving images in real-time, and referencing them again, can be controlled by surveillance cameras.
But the ability of digital optical devices such as surveillance cameras, to pick or identify moving images, or symbols that can identify as one, is the idea of artificial intelligence.
Devices or gadgets to pass as surveillance cameras are tested and trained with the properties of objects to identify and interpret the features and outline of a passing form.
That is because an AI model only operates in terms of the material data it is trained with. In other words, if an attempt is made to optically capture moving figures in real-time, which is what surveillance cameras do, analytical data such as the walking gait of a human, the facial features, physical anatomy, etc. are such parameters that the AI model is “taught” to master.
So, when a person is recognized by an optical sensor, or surveillance camera, and his activities are recorded, it is only made possible with the aid of both machine and deep learning.
10.Image scan and fingerprints
The image scans and fingerprints that you apply before you unlock your smartphones, or doors are all AI initiatives. The idea is to make sure what is private to you cannot be accessed by any other person apart from you. returns!
So, in order to do that, a data item only identical to you and not shared with anyone else is used to render security as some sort of encryption.
Consequently, advanced security came to bare, as technology makers leveraged accessible human biological parts as security bypass or code, which only makes the user rest assured that his device or privacy cannot be encroached upon or invaded except with his permission.
What is reality in the face of artificial intelligence?
The obvious reality in all of these is that it becomes very easy to understand human problems, prescribe methodologies to solve them, and arrive at systematic solutions that can be reused.
Exploiting data makes problem-solving systematic, following procedural models that make the task-in-question interactive and discovery-engendered.
Every factor becomes labeled thereby making it possible to infer possible outcomes that can happen after an occurrence.
The study of AI so much as makes us understand that we have been using several of its models in different sectors even before it became a common word. The attempt to research data in order to make informed decisions in the future is the whole basis on which artificial intelligence is built.
It is from this transformative procedure, that medical practitioners can easily advise on the particular genotype group an individual can engage in marriage with. Based on observed real-life situations, yielding symptoms and dysfunctions, doctors can authoritatively decide that an individual whose genotype is AS should not maritally engage with a partner who is AS. because the outcome is, they’d procreate an SS.
Collecting people’s data has gone beyond placing them in rows and columns, but public agencies such as law enforcement can read from such information the identity of a culprit, even down to his shadow. Internally-identifiable components such as the blood vessels can be analysed and linked to another family member in the event of an emergency call such as leukemia.
Academic research has become more expansive with the creation of ChatGPT, which affords academic-inclined users, and just anybody can consult it to learn more about the subject of his research. It may not ultimately provide the whole content for the research but it can simply act as a work assistant. Bear in mind, that ChatGBT isn’t only meant for academic purposes as people can also utilise artificial intelligence to contribute to inquiries related to health advice, culinary advice, and even tips on best babycare steps.
Artificial intelligence has in fact boosted confidence in mankind as human beings can now predict issues that are crucial to their existence and survival. Issues such as weather forecasts, climate change, population explosion, decline, etc. This makes countries realise what factors are responsible for such issues and the initiatives they have to adopt to mitigate them.
Undoubtedly, AIs have now become ingrained into human aspects it would be difficult to cast it in the background- except, of course, it is the very background on which every industry is now tilting to at the moment.