Kolby  Wyman

Kolby Wyman

1596434760

What Is AI?

There are a lot of articles appearing about “What is AI” (along with “What is machine learning” and “What is reinforcement learning”) that talk about these terms using vague language. Particularly frustrating (at least to me) are the number of authors who equate “AI” with neural networks specifically, or machine learning in general.

We are going to make the distinction between learning and making decisions. We learn from observed data, which is why machine learning is associated with “big data.” Decisions, on the other hand, are designed using models. You cannot learn how to dispatch a fleet of trucks by observing the decisions of dispatchers.

Computer scientists interpret “AI” broadly as capturing any behavior that makes computers behave like humans. In this article, I am going to focus on the tools that are most widely used in business that are described as “AI.” These include

1) Rule-based logic — This was the original form of “artificial intelligence” in the 1960’s and 70’s, but it is still used today. “If furniture has four legs and a seat, it is a chair” or “if the credit score is over 600, grant the loan” are simple examples. In a health context, the attributes of a patient are more complicated (“if patient is male, and if a smoker, and if over 50, and if … and if … then order treatment X”). If the input data (for the “if” statements) has more than three or four dimensions, the rule becomes quickly intractable (this is the well-known “curse of dimensionality”). This is the reason that rule-based AI failed, but we note that simple business rules remain widely used today in virtually all systems.

2) Making estimates using data from the environment — This is the domain of machine learning, also known as statistics, statistical learning, “predictive analytics” and, more broadly, data sciences. It helps to divide machine learning into two broad classes:

a. Supervised learning — This is the domain of “big data” where a large training dataset consisting of input-response (such as, faces with names — the machine learning community refers to the “response” as labels) is used to train a machine learning model.

b. Unsupervised learning — Here we cluster input data (such as attributes of patients or customers) but without access to labels/responses.

3) Making decisions that interact with the environment — Decisions involve a controllable quantity, and a metric that evaluates the performance of the decision. These arise in both static and sequential settings. Algorithmic strategies for making decisions are quite rich, but for this discussion it is useful to identify the following classes of methods:

a. Rule-based logic — We can use rules to make decisions, such as “if a patient has these symptoms, then apply this treatment.”

b. Deterministic optimization — Powerful solvers for high-dimensional problems known as linear programs (where quantities can be continuous) emerged in the 1990s, followed by breakthroughs for integer programs (where quantities have to be discrete) circa 2000. These are widely used in static planning problems such as airline scheduling. Oddly, these tools are not traditionally associated with “AI” (not sure why).

c. Reinforcement learning — These methods emerged in the 1990s in the context of describing how a mouse finds its way out of a maze, and was ultimately applied to the Chinese game of Go. It has been primarily applied to “single entity” problems such as controlling robots, playing games, or guiding a physician, as opposed to more complex resource allocation problems.

d. Stochastic optimization — This is an umbrella term for a wide range of modeling and algorithmic strategies for solving decision problems in the presence of uncertainty. The problem domain is so rich it has been studied by over 15 distinct research communities, spanning problems from optimal stopping to high-dimensional resource allocation problems. Recently, we have pulled these together into a single, unified framework that draws on the tools of deterministic optimization, machine learning and simulation (see the jungle of stochastic optimization).

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What Is AI?
Otho  Hagenes

Otho Hagenes

1619511840

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

Murray  Beatty

Murray Beatty

1598606037

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

This Week in AI - Issue #22 | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.Have fun!

Research Papers

Articles

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

George  Koelpin

George Koelpin

1602255900

Amsterdam And Helsinki Launch Open AI Registers

Amsterdam and Helsinki both launched an Open AI Register at the Next Generation Internet Summit. According to sources, these two cities are the first in the world that are aiming to be open and transparent about the use of algorithms and AI in the cities.

Currently, in the beta version, Algorithm Register is an overview of the artificial intelligence systems and algorithms used by the City of Amsterdam. The register is an effort to show where the cities are currently making use of AI and how the algorithms work.

Jan Vapaavuori, Mayor of Helsinki stated, “Helsinki aims to be the city in the world that best capitalises on digitalisation. Digitalisation is strongly associated with the utilisation of artificial intelligence. With the help of artificial intelligence, we can give people in the city better services available anywhere and at any time. In the front rank with the City of Amsterdam, we are proud to tell everyone openly what we use Artificial Intelligence for.”

#news #ai register #amsterdam ai #helsinki ai #open ai register #ai

AI Artificial Intelligence in business 2020 : Types & Advantages

https://www.mobinius.com/blogs/artificial-intelligence-in-business

#ai artificial intelligence #ai-in-business #ai-development-solutions #ai-app-development #hire-ai-developers