Machine Learning History - What Can We Learn From Deep Blue's Victory?

Deep Blue, NETtalk (Igor Aizenberg’s Word2vec algorithm), Marvin Minsky’s Perceptron, and NETtalk are just a few of the many resources that can be found to learn more about the history behind machine learning. All of these were used in order to make AI better than humans. These were huge breakthroughs in the field of AI, and they all changed the course of history. These groundbreaking technologies are described in detail below.

Deep Blue

The first computer that beat the human world at chess was called Deep Blue. It is considered a major milestone in machine learning. It has been the subject of many books and movies. Deep Blue is now considered the gold standard in machine learning. However, it wasn't always like that. In fact, the human mind is still the best machine learner. What are the lessons to be learned from Deep Blue's win? These are some of the lessons we can learn from this game:

Ray Solomonoff's NETtalk

Ray Solomonoff was a leading figure in machine-learning during the 1950's. Known as the father of artificial intelligence, Solomonoff founded a branch of the field known as machine learning. His work on machine learning, prediction, and probability first gained attention after he circulated a report in 1956. Although he was hospitalized, he was still invited to speak at the AGI 2010 conference in his memory. The event is now called "In Memory Of Ray Solomonoff".

Word2vec algorithm by Igor Aizenberg

Word2vec is a key algorithm in machine learning history. Igor Aizenberg's algorithm laid the foundation for many other highly effective algorithms. The word2vec algorithm is commonly associated with neural networks but it has many other applications in fields like image recognition and computer visual. Also, machine learning algorithms are CNN and LSTM.

Marvin Minsky’s Perceptron

Marvin Minsky is depicted as the villain in the standard history of connectionism. Minsky and co-workers built the first learning' machine, the SNARC, in 1951. Their work was the focus of their Ph.D. dissertation. This article will explore Minsky's contribution to machine learning history. Despite its reputation for being a negative thing, the Perceptron continues to be a vital building block of machine-learning and is one of the most important developments within the field.

ImageNet

In 2008, ImageNet had zero images. By December, it had categorized three million images with more than 6,000 synsets. In April 2010, ImageNet had categorized eleven million images. Crowdsourcing on Mechanical Turk made the challenge possible. The first ImageNet Large Scale Visual Recognition Challenge was held in 2010. Participants were required to classify images. It was a huge success and all the top-scoring competitors were deep neuro networks.

Ray Solomonoff's Inductive Inference Device

Ray Solomonoff's work is known as the Inductive Inference Man. It was his work that led to the creation of deep neural network. Algorithmic Probability is a theory of probability that Ray Solomonoff developed. Five models were presented in his reports which led to 1964. His work helped to create the philosophical basis of the Bayes rule.

FAQ

AI is used for what?

Artificial intelligence refers to computer science which deals with the simulation intelligent behavior for practical purposes such as robotics, natural-language processing, game play, and so forth.

AI can also be referred to by the term machine learning. This is the study of how machines learn and operate without being explicitly programmed.

AI is often used for the following reasons:

1. To make life easier.
2. To be better than ourselves at doing things.

Self-driving cars is a good example. AI is able to take care of driving the car for us.

How does AI function?

To understand how AI works, you need to know some basic computing principles.

Computers store data in memory. Computers interpret coded programs to process information. The code tells a computer what to do next.

An algorithm is an instruction set that tells the computer what to do in order to complete a task. These algorithms are usually written as code.

An algorithm is a recipe. An algorithm can contain steps and ingredients. Each step might be an instruction. One instruction may say "Add water to the pot", while another might say "Heat the pot until it boils."

How will governments regulate AI

Governments are already regulating AI, but they need to do it better. They must make it clear that citizens can control the way their data is used. They must also ensure that AI is not used for unethical purposes by companies.

They should also make sure we aren't creating an unfair playing ground between different types businesses. If you are a small business owner and want to use AI to run your business, you should be allowed to do so without being restricted by big companies.

Statistics

• In 2019, AI adoption among large companies increased by 47% compared to 2018, according to the latest Artificial IntelligenceIndex report. (marsner.com)
• The company's AI team trained an image recognition model to 85 percent accuracy using billions of public Instagram photos tagged with hashtags. (builtin.com)
• More than 70 percent of users claim they book trips on their phones, review travel tips, and research local landmarks and restaurants. (builtin.com)
• By using BrainBox AI, commercial buildings can reduce total energy costs by 25% and improves occupant comfort by 60%. (analyticsinsight.net)
• According to the company's website, more than 800 financial firms use AlphaSense, including some Fortune 500 corporations. (builtin.com)

External Links

medium.com

• 4 Robotic Process Automation Applications (RPA) in the Healthcare Industry
• Numrai

forbes.com

• There is no such thing as the Machine Learning Platform
• Growing at 300% - $8.5 Billion

mckinsey.com

• Survey: AI adoption proves to be worth it, but very few scale impacts
• McKinsey’s latest AI survey finds that there is a steady increase in usage, and it reveals the best practices.

gartner.com

• Gartner Survey shows that 37% of organizations have some AI implementation.
• Innovation Insight: Artificial Intelligence Will Transform Market

How To

How to Set Up Siri To Talk When Charging

Siri is capable of many things but she can't speak back to people. Because your iPhone doesn't have a microphone, this is why. Bluetooth is a better alternative to Siri.

Here's a way to make Siri speak during charging.

1. Under "When Using Assistive touch", select "Speak when locked"
2. Press the home button twice to activate Siri.
3. Siri can be asked to speak.
4. Say, "Hey Siri."
5. Say "OK."
6. You can say, "Tell us something interesting!"
7. Say "I'm bored," "Play some music," "Call my friend," "Remind me about, ""Take a picture," "Set a timer," "Check out," and so on.
8. Speak "Done"
9. If you'd like to thank her, please say "Thanks."
10. If you have an iPhone X/XS or XS, take off the battery cover.
11. Replace the battery.
12. Put the iPhone back together.
13. Connect your iPhone to iTunes
14. Sync your iPhone.
15. Turn on "Use Toggle"