Introduction To Artificial Intelligence (AI)
Artificial intelligence
As we understand intelligence to be described as the ability to perceive or infer information, and to retain it as knowledge to be applied towards adaptive behaviors within an environment or context, Intelligence is the efficiency with which you acquire new skills at tasks you didn’t previously prepare for or have knowledge about, it is also how well and efficient you learn new things and are able to apply them. We might think all of these are attributes that are peculiar only to humans but today Artificial intelligence has changed our view about that.
Artificial intelligence (AI) is a computer system able to perform tasks that ordinarily require human intelligence. AI makes it possible for machines to know from experience, adjust to new inputs and perform human-like tasks, AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage. While Hollywood movies and science fiction novels depict AI as human-like robots that take over the world, the current evolution of AI technologies isn’t that scary — or quite that smart. Instead, AI has evolved to provide many specific benefits in every industry. Every industry has a high demand for AI capabilities — especially question answering systems that can be used for legal assistance, patent searches, risk notification, and medical research, and so on. Typically, AI systems demonstrate at least some of the following behavior associated with human intelligence: planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.
What is AI used for?
AI is essentially a powerful tool that is used by many to improve the way they do things. Many companies employ the use of AI to improve the level at which they render services and satisfy their customers, Company like Amazon have employed the use of AI to improve the level of their customers' experience and satisfaction by reducing the level of navigation through the large number of products they have, the service tends to learn what the customer likes to get and also prompts that every they come in to allow easy usage of their services just like how a shop owner knows what you would like to buy and suggests it to you every time you come in. The following are a few examples of what AI is used for:
· Banking
· Manufacturing
· Retail
· Health care
· Security
· Robotics
· Transportation
· Social Media
· E-commerce
· Marketing
Why is AI important?
AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Backpropagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right, AI achieves incredible accuracy with deep neural networks — which was previously impossible. For example, your interactions with Alexa, Google Search, and Google Photos are all based on deep learning — and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification, and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists, AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots, and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.
How AI can help grow and create employment?
AI can create jobs for many people across various endeavors;
Finance:
Glenn Gow, Partner at Clear Ventures and board member of several AI startups, sees opportunities for AI in finance. “AI has enabled large pieces of the audit process to be automated, but it hasn’t reduced the number of people involved in the audit process. In fact, if done well, AI can help finance professionals find patterns in the data they would otherwise not see identifying questionable transactions, and due to its strength in predictive analytics, create better forecasting models.” This could create additional jobs for auditors who can analyze more data more quickly; then the auditors could focus on helping the company improve processes. This would mean more high value-added consultant auditor positions.
Machine Teachers:
For machine learning, a human teacher is required. A human would flag the millions of data points that are fed into the machine so the machine can learn the patterns. For example, the machine teacher would look at street traffic photos used for autonomous driverless vehicles and distinguish photos of traffic lights versus Christmas lights. The machine would analyze the photos of traffic lights and identify patterns and learn when the vehicle should stop for a traffic light. Reviewing and filtering the data to feed the machine requires thousands of employee-hours. These are typically well-paid positions in areas with low employment and low wages. And with today’s high unemployment, this is good news.
Manufacturing:
Companies that make use of AI in their manufacturing process would definitely need people with the technical know-how to install the new systems and consulting on how to improve processes based on the intelligence/analysis. Of course, the key financial benefit is the improved production process so more life-saving drugs could reach the consumers faster.
How Artificial intelligence works
AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods, and technologies, as well as the following major subfields:
· Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research, and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.
· A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
· Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power, and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.
· Cognitive computing is a subfield of AI that strives for natural, human-like interaction with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech — and then speak coherently in response.
· Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze, and understand images, they can capture images or videos in real-time and interpret their surroundings.
· Natural language processing (NLP) is the ability of computers to analyze, understand, and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.
References:
