Artificial intelligence is a topic that interests the masses, with movies and books dedicated to its advancement. But did you know that AI has already made its way into your daily life? In fact, if you have an iPhone, you’re already using AI to accomplish common tasks, such as your digital assistant Siri or Apple TV search.
Learn more about what artificial intelligence can do for you and how it’s evolving in the future ahead of us—AI-powered options are just around the corner!
Artificial Intelligence, AI for short, is a scientific discipline that goes way beyond just programming computers to do what we tell them.
And that’s why I have written this book. Because everyone needs more than basic arithmetic and logic commands to make the most of their PC and Mac computers. We need help with our programs, instructions on how to make them work for us and not against us.
Have you heard of Artificial Intelligence (AI)?
- Systems that think like humans
- Systems that act like humans
- Systems that think rationally
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. A.I. is also sometimes defined as the ability of a machine to “perceive” and to “understand” human emotion Abstract thought Mainstream definitions
Intelligence exhibits sapience, creativity, or social skills – abilities that humans have but other species lack.
Artificial intelligence encompasses all machines that display intelligent behavior, which includes not only the ability but also the capacity to reason, plan, solve problems, think abstractly, comprehend ideas, learn quickly, and adapt to changing circumstances.
- Systems that act rationally
Artificial intelligence or AI is a computer system that performs functions normally requiring human intelligence such as visual perception, speech recognition, decision-making, and translation between languages.
Examples include IBM’s Deep Blue, the computer system which beat legendary chess champion Garry Kasparov in 1997, Watson, the computer which beat the best players of Jeopardy in 2011, and The Machine, a supercomputer built by IBM.
Alan Turing’s definition would have fallen under the category of “systems that act like humans.”
At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning,
which are frequently mentioned in conjunction with artificial intelligence.
These disciplines are comprised of AI algorithms that seek to create expert systems that make predictions or classifications based on input data.
It is a term used to define the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, and decision-making. An AI system is built for a specific program, with data fed into the machine from various sources.
For example, if you input the data from history books or the Internet on a subject such as World War II, it could learn and accurately predict what will happen next in the sequence.
See reason about artificial intelligence and machine language why we need it
Developing artificial intelligence, in fact, is a way to create a more comfortable life for all of us. We do not mean some kind of alien who would take over the world and make our lives miserable (well, at least we do not expect this to happen).
Really speaking, AI is a program that simulates the intelligent behavior of people or another intelligent creature.
So why do we need it? Because we live in an age when we can build robots that are stronger than humans, or when machines and software help our lives to be more pleasant and beautiful.
Besides these advantages, artificial intelligence also may bring some dangers to us.
Artificial intelligence (AI) is the ability of a machine to imitate the intelligent behavior of humans. It has been growing exponentially since the advent of the computer age and playing chess is a great example of how it is evolving.
Originally, when people started programming computers to play chess, no one could predict just how far AI could reach. Nowadays, thanks to AI and its neural network capabilities.
Computers are defeating even the top human players and chess has become a reliable way to predict how fast AI will progress in other fields as well.
Artificial intelligence and IBM Cloud
IBM has built a supercomputer called Watson that uses artificially intelligent technology. It can understand human speech and learn new things, just like humans do. It can think rationally and make decisions. And it can interact with people naturally, by reading their faces, their tone of voice, and other nonverbal cues.
Artificial intelligence. Self-finding smart systems that are rapidly improving. Systems that are already smarter than humans at a startling rate of speed. These systems are systematically discovering, and have already discovered facts on billions of subjects with accuracy near 100%.
IBM Watson has beaten the best human champions on Jeopardy, predicted the winner of the FIFA World Cup, and is busy transforming industries with its unique cognitive technology. We are now bringing that into IBM Cloud. Get started with IBM Wats…no, sorry, sorry. Get started with a one-click trial.
AI has changed the face of business with its ability to make decisions and recommendations. AI is on the edge of improving every industry from retail to transportation art and manufacturing! From the mundane to the cutting edge, no job is safe from AI even systems from accounting, sales, banking, and beyond are making use of this new technology. AI is not simply a buzzword it’s here and it’s changing the game for everyone!!
Artificial intelligence (AI) is gaining prominence as advances in machine learning, deep learning and cognitive computing technologies have led to a new AI paradigm. IBM Cloud has been at the forefront of this revolution with a comprehensive portfolio of cognitive services that make it easier for developers, data scientists, and business users to understand the value, insights, and complexities contained within their organizations’ data.
Watson is a cognitive platform that uses natural language processing and machine learning to reveal insights from large amounts of data. Watson continuously learns and as it does, gets better at answering questions and discovering correlations between disparate data sets to help scientific investigators, businesses, and consumers make more informed decisions.
We are introducing Watson Tone Analyzer (WTA) for conversational AI in business process apps, new Watson services for healthcare, and new Watson solutions for retailers supplemented by a portfolio update on the IBM Cloud and Channel Strategy.
IBM Cloud is packed with thousands of frameworks, templates, toolkits, and APIs, ready to help you deliver the experiences your customers want. It’s a starter kit for innovators: use it without limits to dream up new applications that cannot be delivered on-premise.
History of artificial intelligence: Key dates and names
Mark the milestones in the history of Artificial Intelligence (AI) with our brief chronological and alphabetical list of names and dates, covering both official definitions and learned society endorsements, plus some entries on forgotten techniques.
The history of artificial intelligence is usually traced to the work of two mathematicians: Alan Turing and John von Neumann. Much of the inspiration for both came from the ideas of Gottfried Leibniz, who envisioned a future where machines could think.
The history of Artificial Intelligence goes as far back as AD 43 when Queen Cleopatra died from a snake bite. This was the first death in recorded history that may have been caused by a computer, but more than one thousand years were to pass before computers came into existence.
Before we delve into the history of artificial intelligence, let us understand what it is.
AI machines are becoming increasingly smarter. We take a look at the evolution of artificial intelligence from the early pioneers like Alan Turing and Shannon to modern-day technologies such as IBM’s Watson
1950 Alan Turing suggests in a seminal paper that machines could eventually be capable of intelligence 1949 Claude Shannon publishes “Programming a computer for playing chess” 1948 Norbert Wiener publishes “Cybernetics: or Control and Communication in the Animal and the Machine.” 1947 First computer, ENIAC, is demonstrated 1946 John von Neumann publishes the paper “The general and logical theory of automata,” laying foundations for artificial intelligence.
1950 Alan Turing publishes Computing machinery and intelligence, introducing the question “Can machines think?
1956 – Turing test passes: artificial intelligence becomes light relief in the public consciousness.
Ruth Gruber, infant bot
I have organized this into a table.
Forecast sales with artificial intelligence
Forecast sales with artificial intelligence: Watson predicts 154% sales increase!
ForecastSales uses artificial intelligence to help sales managers forecast and plan their sales by gaining insights they wouldn’t normally get from standard reports.
Salesforce sales forecasts are a combination of old-fashioned guesswork and intuition built on top of an army of imprecise human interactions, a giant spreadsheet, and your gut. Oh, that’s not how we do it? We make sales forecasting as natural to do for you as writing an email or posting a photo to Instagram. It’s no wonder 80% of our customers choose artificial intelligence over gut-based forecasting.
Predicting sales is always a breeze with Forecast. Data-driven, cloud-based, and built to analyze hundreds of thousands of data points in real-time for any size company or product.
With Kendo’s artificial intelligence technology, you can forecast sales and stocks in real-time. Using machine learning techniques, Kendo’s neural networks uncover hidden insights in your data.
There’s an app for predicting the weather. And road traffic. Why not for sales? With Jumpshot, there is: we’re an artificial intelligence (AI) solution that forecasts how businesses are going to perform in real-time, allowing them to prepare in advance.
So you want to predict sales? Would it help if you could see what customers were planning to buy 5 months from now? Use our artificial intelligence algorithms, built on millions of actual customer purchase predictions, to discover new trends and uncover hidden insights in your market.
We know the business. And we know numbers. So we did the math and figured out that data plus imagination can equal sales. Now, every business has an AI bot in its corner — and ours is Lightning. We combine the power of real-time, data-driven forecasting with artificial intelligence to drive revenue for our clients through their own unique path to purchase.
It’s high time sales forecasting got a 21st-century makeover. Instead of making questionable assumptions and staying up late with spreadsheets and calculators, let SALESmanago forecast your sales for you.
Sales are unpredictable, yes? So why are you forecasting with spreadsheets?
Today’s forecast? We’re predicting that AccuWeather will be the most impactful weather data and content platform to watch this quarter. Our 8 million global users visited our platform 124 billion times in the last year. And according to a study we recently conducted across Gartner clients, AccuWeather is the leading weather provider used by retail brands to drive activities, from merchandising and ad buying to mobile location services.
What if you could predict the future?
We may not have the ability to tell you about your love life or where you’ll travel next year. But we can give you access to one of the most valuable and powerful tools ever invented. Our very own artificial intelligence-based Sales Navigator gives salespeople everything they need, right in their inboxes. It’s free and allows you to track deals, manage leads and make progress with intelligent real-time notifications–all while staying organized and on task.
Artificial intelligence applications
There are numerous, real-world applications of AI systems today. Below are some of the most common examples:
- Speech recognition: It is also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, and it is a capability that uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting.
- Customer service: Online virtual agents are replacing human agents along the customer journey. They answer frequently asked questions (FAQs) around topics, like shipping, or provide personalized advice, cross-selling products, or suggesting sizes for users, changing the way we think about customer engagement across websites and social media platforms. Examples include messaging bots on e-commerce sites with virtual agents, messaging apps, such as Slack and Facebook Messenger, and tasks usually done by virtual assistants and voice assistants.
- Computer vision: This AI technology enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs, and based on those inputs, it can take action. This ability to provide recommendations distinguishes it from image recognition tasks. Powered by convolutional neural networks, computer vision has applications within photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry.
- Recommendation engines: Using past consumption behavior data, AI algorithms can help to discover data trends that can be used to develop more effective cross-selling strategies. This is used to make relevant add-on recommendations to customers during the checkout process for online retailers.
- Automated stock trading: Designed to optimize stock portfolios, AI-driven high-frequency trading platforms make thousands or even millions of trades per day without human intervention.
As conversations emerge around the ethics of AI, we can begin to see the initial glimpses of the trough of disillusionment. To read more on where IBM stands within the conversation around AI ethics, read more here.
Deep learning vs. machine learning
See many examples of deep learning vs. machine learning
Deep learning vs. machine learning: both sub-fields of artificial intelligence, with deep learning being a sub-field of machine learning. Whew! It can be complicated to wrap your head around, to say the least. In fact, the two are confused on a regular basis. Let’s go over what they mean so they’ll become clearer to you.
Hey, Google, what’s the difference between deep learning and machine learning? -machine-learning uses patterns to make predictions, whereas deep learning uses architectures of networks, and no, they’re not the same thing.
Ok, but they really are technically almost the same thing because deep learning is just a part of machine learning. That’s right. Even though you may use each of these almost synonymously and interchangeably in regular conversation, we know that there are actually a lot of details that separate them.
Sorry to break it to you machine-learning enthusiasts, but deep learning is not so much a sub-field of machine learning as it is a whole new branch of artificial intelligence altogether. Machine learning relies on algorithms that do no more than sort through mountains of data and look for patterns. artificial intelligence stocks
Deep learning, on the other hand, is about getting computers to use those patterns in order to actually process information, be it grasping an object or recognizing a face.
Deep learning vs. machine learning… why do they get confused so often? It’s truly one of the great mysteries of the 21st century.
Machine learning and deep learning are often used interchangeably, as they’re both subfields of AI. But to evaluate the two, it’s worth considering their core differences.
What’s the difference between deep learning and machine learning?
For laymen, machine learning is when a computer teaches itself how to play chess, for example. Deep learning is like giving the computer glasses so it can silently observe the rest of the game, then helping it learn what move to make.
Like many terms in the tech industry, “machine learning” is being used as a buzzword to sell products (specifically, to sell you on their AI platforms). To some degree, this article is doing that too. Deep learning and machine learning are both artificial intelligence techniques!
If you like memes and gifs, then you’ve probably stumbled upon a few about “learning” at some point. But have you ever wondered what the difference between machine learning and deep learning is? Turn on the light and let’s dive in!
Deep versus machine learning: It’s the ultimate battle of man versus machine. Now, before you write in to call me a Luddite, note that I am referring to two distinct disciplines, and it’s possible (and likely) that one is not a threat to overthrow the other in some sort of Terminator/Skynet scenario.
Similar terms, different approaches. Let’s look at how deep learning and machine learning are similar, and how they differ.
But how do you tell the two apart? Machine learning refers to the broad set of algorithms used to train computers to make predictions given certain pieces of data. Deep learning, on the other hand, is a specific branch of machine learning that can be likened to “learning by example.”
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