Category Archives: Artificial Intelligence

NEW DELHI: A newly devised algorithm powered by artificial intelligence can help increase the predictability of the Indian Summer Monsoons (ISMR), 18 months ahead of the season.The algorithm called predictor discovery algorithm (PDA) made using a single ocean-related variable could facilitate skillful forecast of the ISMR in time for making effective agricultural and other economic plans for the country, according to the ministry of science and technology.Scientists at the Institute of Advanced Study in Science and Technology (IASST), Guwahati, an autonomous institute of department of science and technology (DST), along with their collaborators have found that the widely used sea surface temperature (SST) is inadequate for calculation of long-lead prediction of ISMR. This, they found was because the potential skill of ISMR estimated by the predictor discovery algorithm (PDA) using SST-based predictors was low at all the lead months.The team consisting of IASST, Indian Institute of Tropical Meteorology (IITM), Pune, and Cotton University, Guwahati, devised a predictor discovery algorithm (PDA) that generates predictor at any lead month by projecting the ocean thermocline depth (D20) over the entire tropical belt between 1871 and 2010 onto the correlation map between ISMR and D20 over the same period.The new algorithm indicates that the potential skill of ISMR is maximum (0.87, highest being 1.0), 18 months before the ISMR season. At any lead month, the predictability of the annual variability of ISMR depends on the degree of regularities in the annual variability of its drivers.With the newly discovered basis of long-lead ISMR predictability in place, Devabrat Sharma (IASST), Santu Das (IASST), Subodh K. Saha (IITM), and B N Goswami (Cotton University) were able to make 18-month lead forecast of ISMR between 1980 to 2011 with an actual skill of 0.65 using a machine learning-based ISMR prediction model.According to the statement of the ministry, the success of the model was based on the ability of artificial intelligence (AI) to learn the relationship between ISMR and tropical thermocline patterns from 150 years of simulations by 45 physical climate models and transferring that learning to actual observations between 1871 and 1974.As the potential skill of ISMR at 18-month lead is 0.87, there is still considerable scope in improving the model.

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Artificial intelligence helps improve predictability of Indian summer monsoons - Times of India

What is the role of artificial intelligence in the financial services industry?

AI is proving to be a powerful tool for financial institutions looking to improve their operations, manage risks, and optimize their portfolios more effectively.

Artificial intelligence (AI) is playing an increasingly vital role in the financial services industry. Predictive analytics, which can assist financial firms in better understanding and anticipating client demands, preferences and behaviors, is one of the most well-known uses of AI. They can then use this information to create goods and services that are more individually tailored.

Moreover, AI is also being utilized to enhance risk management and fraud detection in the financial services industry. AI systems can swiftly identify unusual patterns and transactions that can point to fraud by evaluating massive amounts of data in real-time. This can assist financial organizations in reducing overall financial risk and preventing fraud-related losses.

In addition, AI is being used for portfolio optimization and financial forecasting. By utilizing machine learning algorithms and predictive analytics, financial institutions can optimize their portfolios and make more accurate investment decisions.

Machine learning, deep learning and NLP are helping financial institutions improve their operations, enhance customer experiences, and make more informed decisions. These technologies are expected to play an increasingly significant role in the finance industry in the coming years.

Financial organizations may make better decisions by using machine learning to examine massive volumes of data and find trends. For instance, machine learning can be used to forecast stock prices, credit risk and loan defaulters, among other things.

Deep learning is a subset of machine learning that utilizes neural networks to model and resolve complicated issues. For instance, deep learning is being used in finance to create models for detecting fraud, pricing securities and managing portfolios.

Natural language processing (NLP) is being used in finance to enable computers to understand human language and respond appropriately. NLP is used in financial chatbots, virtual assistants and sentiment analysis tools. It enables financial institutions to improve customer service, automate customer interactions and develop better products and services.

AI is proving to be a powerful tool for financial institutions looking to improve their fraud detection and risk management processes, enabling them to operate more efficiently and effectively while minimizing potential losses.

Here are the steps explaining how AI helps in fraud detection and risk management in financial services:

Chatbots and virtual assistants are proving to be valuable tools for financial institutions looking to improve the customer experience, reduce costs and operate more efficiently.

Chatbots and virtual assistants are utilized to provide individualized services and assistance, which enhances the client experience. Customers can communicate with these AI-powered tools in real-time and receive details on their accounts, transactions and other financial services. They can also be used to respond to commonly asked inquiries, offer financial counsel and assist clients with challenging problems.

Suppose a bank customer wanted to check their account balance or ask a question about a recent transaction, but the banks customer service center was closed. The customer can make use of the banks chatbot or virtual assistant to receive the information they require in real-time rather than having to wait until the following day to speak with a customer support agent.

The virtual assistant or chatbot can verify the customers identification and give them access to their account balance or transaction details. If the customer has a more complex issue, the chatbot or virtual assistant can escalate it to a human representative for further assistance. This means that AI-powered chatbots and virtual assistants can provide immediate responses to customer inquiries, reducing wait times and improving customer satisfaction.

Because they are accessible round-the-clock, chatbots and virtual assistants are useful resources for clients who require support outside of conventional office hours. Through the automation of repetitive processes and the elimination of the need for human support, they can also assist financial organizations in cutting expenses.

The financial services industry can enjoy several benefits from AI systems, such as automating mundane tasks, improving risk management and swift decision-making. Nevertheless, the drawbacks of AI, such as security risks, potential bias and absence of a human touch, should not be ignored.

Potential advantages of AI in the financial services industry include:

The possible disadvantages of using AI in the financial services industry consist of:

The future of AI in finance is exciting, with the potential to improve efficiency, accuracy and customer experience. However, it will be essential for financial institutions to carefully manage the risks and challenges associated with the use of AI.

The use of AI in financial services has the potential to significantly improve the sector. Several facets of finance have already been transformed by AI, including fraud detection, risk management, portfolio optimization and customer service.

Automating financial decision-making is one area where AI is anticipated to have a large impact in the future. This could involve the examination of massive amounts of financial data using machine learning algorithms, followed by the formulation of investment recommendations. With AI, customized investment portfolios might be constructed for clients depending on their risk appetite and financial objectives.

In addition, AI-powered recommendation engines could also be developed to offer customers targeted products and services that meet their needs. This could improve customer experience and satisfaction while also increasing revenue for financial institutions.

However, there are also potential challenges associated with the use of AI in finance. These include data privacy concerns, regulatory compliance issues, and the potential for bias and discrimination in algorithmic decision-making. It will be important for financial institutions to ensure that AI is used in a responsible and ethical way and that appropriate safeguards, such as transparent algorithms and regular audits, are in place to mitigate these risks.

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How is artificial intelligence revolutionizing financial services? - Cointelegraph

Tech expert Jessica Melugin discusses Twitter CEO Elon Musk's concerns about artificial intelligence and his claims the U.S. government had access to Twitter DMs on 'The Evening Edit.'

Coca-Cola has joined the list of companies integrating the power of artificial intelligence into its services and products.

In a first-quarter earnings release, the soft drink company said it was adopting emerging technologies like AI to "drive new approaches, more experimentation and improved speed to market."

FILE: Coca-Cola bottles sit in a delivery truck in Mexico City, Mexico, on Thursday, Sept. 5, 2013. (Photographer: Susana Gonzalez/Bloomberg via Getty Images / Getty Images)

The company highlighted its collaboration with OpenAI and the consulting firm Bain & Company to experiment with ChatGPT and DALL-E "to enhance marketing capabilities and business operations and to build capabilities through cutting-edge [AI]."

Within a month of this partnership, Coca-Cola launched the "Create Real Magic" platform, which allowed consumers to create original artwork with assets from the companys archives.

US NAVY TO USE UNMANNED, AI-DRIVEN SHIPS TO COUNTER SMUGGLING, ILLEGAL FISHING

Coca-Cola said it was also experimenting with ways to leverage AI "to improve customer service and ordering as well as point-of-sale material creation in collaboration with its bottling partners."

Amid the rapid rise of AI, companies and employees alike are increasingly using AI-powered tools like ChatGPT for everyday operations.

FILE: Coca-Cola bottles are seen in this illustration photo taken in Krakow, Poland. (Photo Illustration by Jakub Porzycki/NurPhoto via Getty Images / Getty Images)

Coca-Cola competitor PepsiCo has begun incorporating AI into its processes for tracking consumer demand and new product development, helping the company boost sales and bring new products to customers faster.

Per its earnings report, Coca-Cola reported higher-than-expected sales in the first quarter as it continued to hike prices and its business in China improved.

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The company said revenue rose 5% to $11 billion for the January-March period, beating Wall Street's expectations.

FOX Business Breck Dumas and Eric Revell contributed to this report.

Originally posted here:

Have AI and a smile: Coca-Cola leveraging artificial intelligence to improve customer service, ordering - Fox Business

AITX's Subsidiary, Robotic Assistance Devices, Releases Exclusive AI-Powered Tracking Feature

Company Expected to File Patent Application on Its Advanced Analytics

Detroit, Michigan, April 27, 2023 - Artificial Intelligence Technology Solutions, Inc., (the "Company") (OTCPK:AITX), a global leader in AI-driven security and productivity solutions along with its wholly owned subsidiary, Robotic Assistance Devices, Inc. (RAD), today released a new AI-based analytic providing enhanced tracking of vehicles and humans.

This unique analytic is being added to the list of provisional patent applications the Company plans on filing in the near future. Analytics refers to the process of identifying, interpreting, and communicating significant or meaningful patterns of collected data.

This new analytic solves two challenges important to end users and security operations centers, specifically the reduction of false positive alarms, and the introduction of advanced functionality that can be used to detect stolen cars, new vehicles and persons entering spaces with existing cars and persons. The Company believes that an analytic with this functionality does not exist in the marketplace, although it may.

The Company noted that this tracking functionality has been successfully tested at several client locations across the US and is now being made available to all subscribed RAD clients. The Company has identified 31 ROSA devices that the analytic will be deployed on in the coming weeks. The addition of this analytic strengthens RAD's value to its end users, increasing retention and exclusivity.

Tracking is part of a suite of internally developed AI analytics which includes human detection, vehicle detection, license plate recognition, and firearm detection. Tracking is included with a subscription to any RAD device, including RAD's top-selling solution ROSA (Responsive Observation Security Agent).

"Although we deploy hardware as a device-as-a-service model, at our core AITX is a software company," said Steve Reinharz, CEO of AITX and RAD. "Surveillance systems have struggled to reduce what are referred to as 'false positives', where slight or typical movement of an object triggers an alert. This is particularly troublesome when observing the movement, or non-movement of vehicles. This new analytic knows when a vehicle is on the move or remaining in place. We have made incredible advancements to reduce the instances of false positives."

"We know that every client is unique and there's no 'one size fits all' approach to how they will utilize our solutions," commented Mark Folmer, CPP, FSyI, President of RAD. "The vehicle tracking analytic is completely customizable by the end-user or authorized remote monitoring service. It's our objective to make deploying RAD solutions a seamless and easy process for our clients and channel partners."

"AITX has made substantial investments in R&D and software development," added Reinharz. "To help protect these investments, we have carefully reviewed the need for patents in each area of research to formulate more effective R&D strategies. We've identified the projects where the Company should file relevant patent applications as necessary to help build a strong global intellectual property portfolio."

The Company expects to provide future announcements regarding the status of the aforementioned patent applications.

ROSA is a multiple award-winning, compact, self-contained, portable, security and communication solution that can be installed and activated in about 15 minutes. Like other RAD solutions, it only requires power as it includes all necessary communications hardware. ROSA's AI-driven security analytics include human, firearm, vehicle detection, license plate recognition, vehicle tracking, responsive digital signage and audio messaging, and complete integration with RAD's software suite notification and autonomous response library. Two-way communication is optimized for cellular, including live video from ROSA's dual high-resolution, full-color, always-on cameras. RAD has published three Case Studies detailing how ROSA has helped eliminate instances of theft, trespassing and loitering at car rental locations and construction sites across the country.

AITX, through its subsidiary, Robotic Assistance Devices, Inc. (RAD), is redefining the $25 billion (US) security and guarding services industry through its broad lineup of innovative, AI-driven Solutions-as-a-Service business model. RAD solutions are specifically designed to provide cost savings to businesses of between 35%-80% when compared to the industry's existing and costly manned security guarding and monitoring model. RAD delivers this tremendous cost savings via a suite of stationary and mobile robotic solutions that complement, and at times, directly replace the need for human personnel in environments better suited for machines. All RAD technologies, AI-based analytics and software platforms are developed in-house.

RAD has a prospective sales pipeline of over 35 Fortune 500 companies and numerous other client opportunities. RAD expects to continue to attract new business as it converts its existing sales opportunities into deployed clients generating a recurring revenue stream. Each Fortune 500 client has the potential of making numerous reorders over time.

CAUTIONARY DISCLOSURE ABOUT FORWARD-LOOKING STATEMENTS

The information contained in this publication does not constitute an offer to sell or solicit an offer to buy securities of Artificial Intelligence Technology Solutions, Inc. (the "Company"). This publication contains forward-looking statements, which are not guarantees of future performance and may involve subjective judgment and analysis. The information provided herein is believed to be accurate and reliable, however the Company makes no representations or warranties, expressed or implied, as to its accuracy or completeness. The Company has no obligation to provide the recipient with additional updated information. No information in this publication should be interpreted as any indication whatsoever of the Company's future revenues, results of operations, or stock price.

About Artificial Intelligence Technology Solutions (AITX)

AITX is an innovator in the delivery of artificial intelligence-based solutions that empower organizations to gain new insight, solve complex challenges and fuel new business ideas. Through its next-generation robotic product offerings, AITX's RAD, RAD-M and RAD-G companies help organizations streamline operations, increase ROI, and strengthen business. AITX technology improves the simplicity and economics of patrolling and guard services and allows experienced personnel to focus on more strategic tasks. Customers augment the capabilities of existing staff and gain higher levels of situational awareness, all at drastically reduced cost. AITX solutions are well suited for use in multiple industries such as enterprises, government, transportation, critical infrastructure, education, and healthcare. To learn more, visit http://www.aitx.ai, http://www.stevereinharz.com, http://www.radsecurity.com, http://www.radgroup.ai, and http://www.radlightmyway.com, or follow Steve Reinharz on Twitter @SteveReinharz.

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Steve Reinharz

949-636-7060@SteveReinharz

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Artificial Intelligence Technology : AITX's Subsidiary, Robotic Assistance Devices, Releases Exclusive AI-Powered Tracking Feature Company Expected to...

(A Lesson from IIT is a weekly column by an IIT faculty member on learning, science and technology on campus and beyond. The column will appear every Friday.)

Sutanu Chakraborty

The quest for building machines that think and act like us has propelled significant advances in Artificial Intelligence (AI). While we now have systems that do restricted tasks very well, the vision of Artificial General Intelligence (AGI), where machines can seamlessly learn and do anything that a human does, continues to elude us. But ChatGPT is around and it seems magical, right? Does it have AGI? Not quite.

For the uninitiated, ChatGPT is powered by technology that belongs to the family of Large Language Models (LLMs). A language model can tell us that a cat is sitting on the mat is better English than a cat is sitting in the mat. If given the last few words in a sentence, a language model can also predict which word is most likely to come next. You can think of a language model as a black box with a lot of numbers, called parameters. These parameters implicitly capture diverse aspects of language such as grammar, word usage and even world knowledge (I like noodles with sauce is more likely than I like noodles with pizza, for instance).

Any sentence that you type in at the ChatGPT prompt is converted into a set of numbers that interact with the parameters of the language model to finally yield another set of numbers, which are rendered as output text. Large Language Models have on the order billions of parameters that are learnt from really large volumes of data. An example is all of the textual content that can be scraped from the entire web.

As part of training ChatGPT, it was also ensured that the system learns from human feedback. As a consequence, it got rewards for doing its job very well and was punished otherwise. Consequently, the end result is impressive: this new tribe of AI technologies is undoubtedly disruptive in more ways than we could have imagined. ChatGPT excels at many jobs humans traditionally take pride in writing poems, code, online web content and so on. Do many of us then end up losing our jobs? Are such fears well founded?

First things first, LLMs, at their very core, are not as smart as they appear to be. Consider the case of two kindergarten kids arguing on whether the movie Titanic is romantic or tragic. Neither of them has experienced either tragedy or romance. The debate is literally a war of words, purely based on what they heard their parents talk about. As they grow up, they realise that both of those things were right after all Titanic is tragic and romantic at the same time. In a way not very different from the kindergarten kids, LLMs spits out words. However, the meanings of those are not grounded in experiences.

We must, therefore, not lose sight of the fact that LLMs lack a robust theory of the world. We should not be surprised if a six-year old beats ChatGPT in tasks that demand common-sense reasoning and basic logical inferencing. To quote the noted linguist Noam Chomsky, LLMs are incapable of distinguishing the possible from the impossible. Consequently, they have the propensity to fabricate things and generate factually incorrect or biased responses that are not meant for serious professional consumption.

In the context of software jobs, LLM models can write functions or boilerplate code given a well-defined goal, but may not be able to factor vaguely-specified high level business goals into components that need to be designed. They may also not be able to analyse how these components should interact, prescribe how best to leverage competencies of the existing workforce to get the whole job executed in a given timeframe, and suggest ways of recovering from aberrations in case plans do not get executed as expected.

Edsger Dijkstra, a stalwart in the field of computing, had famously observed that computer science should be called computing science for the same reason why surgery is not called knife science. In the context of programming, ChatGPTcan help us code faster and thereby get our tools ready, but we cannot effectively use them unless we have a good grasp of the pathology of the problem we have set out to solve.

The post-LLM age will trigger the shift from a tool-centric approach to a problem-centric one. Stuart Russell, a leading AI expert, observes that despite all advances, AI systems need to be explicitly provided with an objective. As humans, not only are we aware of all we need to do to get a job done, we carry strong normative assessments of all that should not be done.

We need people to figure out what to do. Machines can help us with the how as long as people are at the wheels. And in this new age, those with a good mix of wisdom to make best use of the technological resources at hand and strong interpersonal skills will be well sought after.

History is testimony to the fact that technological innovation initially displaces workers but creates fresh avenues for employment in the long run. A recent study by economist David Autor and others reveal that more than half of workers today are employed in occupations that did not exist in 1940.Amidst growing concerns of layoffs in major software industries in the near future, Geoffrey Hinton, one of the pioneers of the deep learning revolution that led to the creation of LLMs, opines that we could alternately retain the same workforce, and target achieving a lot more, by leveraging the leap in productivity.

Over time, we are likely to see a flurry of new jobs that do not exist today. In my childhood, I would fancy winning quiz competitions by memorising facts. With Google around, such faculties are no longer held in high esteem. The yardstick of competence has evolved today, students are assessed on the basis of their critical thinking, creativity and argumentative skills instead. As technology evolves, we will have to adapt to newer ways of re-evaluating ourselves.

In this age of fierce competition, it is important to remind ourselves that each of us is uniquely gifted. Career choices need to be made carefully so that they align with ones natural instincts and are not merely driven by societal pressures. This will make sure we enjoy the job we do at the very least, this can surely set us apart from ChatGPT and its future incarnations.

A story goes that Albert Einsteins chauffeur who had heard Einstein lecturing so many times over that he felt confident he could do the job of Einstein. The legendary physicist offered the chauffeur an opportunity to lecture, put on the chauffeurs attire and occupied one of the rear benches. The chauffeur did a fabulous job and skillfully answered a few questions as well. However, when a rather esoteric question on anti-matter that seemingly digressed away from the main theme came up, the chauffeur replied, Sir, this is so simple, Ill let my chauffeur seated at the back answer it on my behalf.

Like the chauffeur, LLMs are exposed to content very much a product of human thought but cannot substitute an expert who has first-hand experience of the process by which such content came into being. On the other hand, a single human beings range of expertise is miniscule compared to the wide expanse of content that fuels ChatGPT. The future is about exploring interesting ways in which machines can complement and augment our abilities, not substitute them. The new generation will adapt a lot faster to this change, since they would not carry the baggage of how things were done in the past this seamless coevolution of humans with technology, would be, for them, the norm.

We must embrace the new age with the readiness not only to do things differently but also to do different things.

(The writer is a professor at the department of Computer Science and Engineering at IIT Madras. He is part of the Artificial Intelligence and Databases (AIDB) Lab.)

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Will we lose jobs to Artificial Intelligence? Are such fears well founded? IIT Madras professor explains - The Indian Express

The market experienced a significant shift on Tuesday. There were several weak reports like that from UPS (UPS) , and a number of strong reports like McDonald's (MCD) and General Motors (GM) as well, but the market gapped lower at the open and trended down the rest of the day.

The selling was very broad and persistent, and the dip buyers that have been so active lately stood on the sidelines and watched.

It looked quite gloomy at the close, but Microsoft (MSFT) posted an extremely strong report, and Alphabet (GOOGL) announced a substantial buyback of shares. This action is producing a substantial bounce in the Nasdaq 100 (QQQ) , which was trading up about 1% after dropping 1.9% on Tuesday.

There are still hundreds of earnings reports to come, including heavyweights like Meta (META) , Amazon (AMZN) , and Apple (AAPL) , but will they help to shore up the broad damage that is occurring in other areas of the market like Semiconductors (SMH) and Financials (XLF) ?

The problem is that the stellar report from Microsoft, and to a lesser degree Google, is company specific. Both companies are benefiting from a boom in artificial intelligence (AI). The growth there is even faster than what occurred during the internet bubble in the late 1990s, and Microsoft is the leader.

AI is going to benefit many companies in various ways, but it is not going to stop the economic cycle. The shift in market action on Tuesday was largely due to concerns about banks because of the collapse of First Republic Bank (FRC) and growing concern about economic growth. A poor report from UPS and broad weakness in trucking indicated that the economy is slowing very quickly. A poor Philly Fed report and other economic news is also a sign that things are slowing.

Another indication that a major shift is occurring is that bonds rallied sharply as equities fell and money flowed into safe plays like soft drinks and pharmaceuticals. There was a major rotation out of the stocks that are most likely to suffer from a recession, such as small-caps (IWM) and chips, and into the safety of bonds and drugs.

We have a slew of earnings ready to hit, and we will see how far Microsoft can lead the market, but the danger lies in thousands of smaller stocks that will be offset to some extent by Google and Microsoft.

(UPS, MSFT, GOOGL, AMZN and AAPL are holdings in the Action Alerts PLUS member club. Want to be alerted before AAP buys or sells these stocks? Learn more now.)

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Will Microsoft and Artificial Intelligence Save the Market? - RealMoney

The Town of Hay River says it is trialling artificial intelligence to help prepare material for some of its communications with residents.

Assistant senior administrative officer Patrick Bergen told town councillors on Monday that the municipality is testing the use of artificial intelligence in some circumstances.

Various AI applications have made headlines in recent months, from tools like Midjourney which can create realistic images based on text prompts to ChatGPT, which provides answers to users questions in a form of online conversation.

AI is also being introduced to search engines. Some Bing users can now search the web using an AI interface, while Google is carrying out limited testing of an equivalent tool named Bard.

On a broad level, concerns remain that AI tools to search the web or generate answers arent always accurate. They sometimes reproduce false or misleading information found online, an error known as hallucination.

But having been trained on a vast online resource of written material, AI apps are increasingly used to handle tasks like editing text or writing summaries of dense information.

The system is good at crunching large of pieces of information into something readable, Bergen said.

Its also good at grouping large amounts of data and summarizing it. That will speed up some of the releases that go out.

Its not clear if any of the towns communications to date have been published with the assistance of artificial intelligence.

Bergen told councillors that recent requests from journalists had been fairly routine in that they focused on breakup season, which has proceeded quietly to date, and were handled by senior administrator Glenn Smith.

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Hay River testing artificial intelligence for communications - Cabin Radio

1. AI training pause? Americans say artificial intelligence tech shouldn't be restrained  Fox News
2. Facebook chief Zuckerberg consumed by race to launch AI in snub to Musk-backed pause  Fox Business
3. Editorial: Tech must craft AI safety protocols, forget naive call for pause  The Mercury News

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AI training pause? Americans say artificial intelligence tech shouldn't be restrained - Fox News

Artificial Intelligence in Food and Beverage Market Size Set to Skyrocket with Projected CAGR of 45.4% from  EIN News

Originally posted here:

Artificial Intelligence in Food and Beverage Market Size Set to Skyrocket with Projected CAGR of 45.4% from - EIN News

A common type of training model in AI is an artificial neural network, a model loosely based on the human brain.

A neural network is a system of artificial neuronssometimes called perceptronsthat are computational nodes used to classify and analyze data. The data is fed into the first layer of a neural network, with each perceptron making a decision, then passing that information onto multiple nodes in the next layer. Training models with more than three layers are referred to as deep neural networks or deep learning. Some modern neural networks have hundreds or thousands of layers. The output of the final perceptrons accomplish the task set to the neural network, such as classify an object or find patterns in data.

Some of the most common types of artificial neural networks you may encounter include:

Feedforward neural networks (FF) are one of the oldest forms of neural networks, with data flowing one way through layers of artificial neurons until the output is achieved. In modern days, most feedforward neural networks are considered deep feedforward with several layers (and more than one hidden layer). Feedforward neural networks are typically paired with an error-correction algorithm called backpropagation that, in simple terms, starts with the result of the neural network and works back through to the beginning, finding errors to improve the accuracy of the neural network. Many simple but powerful neural networks are deep feedforward.

Recurrent neural networks (RNN) differ from feedforward neural networks in that they typically use time series data or data that involves sequences. Unlike feedforward neural networks, which use weights in each node of the network, recurrent neural networks have memory of what happened in the previous layer as contingent to the output of the current layer. For instance, when performing natural language processing, RNNs can keep in mind other words used in a sentence. RNNs are often used for speech recognition, translation, and to caption images.

Long/short term memory (LSTM) are an advanced form of RNN that can use memory to remember what happened in previous layers. The difference between RNNs and LTSM is that LTSM can remember what happened several layers ago, through the use of memory cells. LSTM is often used in speech recognition and making predictions.

Convolutional neural networks (CNN) include some of the most common neural networks in modern artificial intelligence. Most often used in image recognition, CNNs use several distinct layers (a convolutional layer, then a pooling layer) that filter different parts of an image before putting it back together (in the fully connected layer). The earlier convolutional layers may look for simple features of an image such as colors and edges, before looking for more complex features in additional layers.

Generative adversarial networks (GAN) involve two neural networks competing against each other in a game that ultimately improves the accuracy of the output. One network (the generator) creates examples that the other network (the discriminator) attempts to prove true or false. GANs have been used to create realistic images and even make art.

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What Is Artificial Intelligence (AI)? | Google Cloud