Generative AI: What Is It, Tools, Models, Applications and Use Cases

Top Generative AI Applications & Use Cases of 2023

Businesses across diverse industries can leverage this technology to improve efficiency, save resources, and optimize performance. Drawing inspiration from these use cases is the first step toward integrating AI within a company and achieving superior results across various aspects of daily operations. For instance, generative AI can be used to examine machine sensor data and forecast when a failure is most likely to occur. This enables equipment manufacturers to plan maintenance and repairs in advance, cutting downtime and enhancing overall equipment performance.

• It’s an AI app designed for visually impaired individuals that harnesses the power of GPT-4 to convert images into text instantly.
• The company showcased 10 AI projects during the conference, such as Project Clever Composites, which simplifies the tedious process of compositing images into backgrounds.
• With a combination of documents, videos, and vetted data sources, Farmer.CHAT delivers actionable recommendations to farmers in India, Ethiopia, and Kenya.

In addition, generative AI models also rely on training data for learning about the rules and patterns in natural language. After training, the generative AI models could generate new text that features a similar style and tone as the input data. In the media industry, combining machine learning techniques with marketing techniques can lead to improved content generation. Predictive targeting is an example of a marketing technique that utilizes both AI and machine learning, foreseeing a customer’s next decision by analyzing their old data and behavior patterns. Synthesia is another example of a well-known generative AI company that implements new synthetic media technology for visual content creation, and it does so by using minimum skills and cost. By utilizing generative AI, both marketing and advertising industries can produce more personalized content, they can better understand the market and consumer behavior, and come up with more efficient campaigns.

Experience Generative AI at the NVIDIA AI Playground

From drug discovery and development to improved medication plans and personalized treatment recommendations, algorithms can assist medical specialists in aiding patients and finding new ways to cure diseases. If this technology had been as advanced a few years ago, who knows how the COVID-19 pandemic outbreak would have been handled? Art and business intersect in many ways, and AI can also provide support in this area.

The same is true of generative AI, which software developers apply to automate manual coding. Humans “explain” to a specifically-honed solution what they want to obtain, and the machine churns out the requested programs in necessary quantities. Many old movies and classic Disney cartoons belong to the treasury of global culture, but their quality often fails the imperatives of our time. Generative AI can upscale them to 4k and even more, generate 60 frames per second instead of the conventional 23, remove noise, and transform black-and-white into color. Generative AI-fueled tools can detect malicious or at least suspicious activities in no time and prevent all kinds of damage to a business or a person.

Neural networks, which form the basis of much of the AI and machine learning applications today, flipped the problem around. Designed to mimic how the human brain works, neural networks “learn” the rules from finding patterns in existing data sets. Developed in the 1950s and 1960s, the first neural networks were limited by a lack of computational power and small data sets.

Corporate leaders are using the deep learning model’s capability to canvas large amounts of data and provide logical responses. So if you’re a developer, it’s time to join the AI revolution and start building interesting AI applications! You can try Yakov Livshits out these models in Google Colab or other collaborative data science notebooks. When you’re building applications using large language models, you should be aware of the common pitfalls, such as factually incorrect information and hallucinations.

Generative AI Application in Manufacturing

Furthermore, for pharmaceutical companies, Generative AI can be used to analyze large data sets on drug interactions, side effects, and efficacy, helping in drug discovery and repurposing. The importance of Generative AI in the healthcare industry cannot be overemphasized. Generative AI can assist radiologists in detecting cancer, heart diseases, and neurological disorders by analyzing medical images, such as X-rays, CT scans, and MRIs. This way, diagnoses can be made more accurately and are less likely to be missed or delayed.

On the other hand, GANs work for generative multimedia and visual content from images and text. Conversational AI tools can be trained on a variety of languages, and it can translate messages from one language to another in real-time. By using machine learning algorithms, manufacturers can predict equipment failures and maintain their equipment proactively. These models can be trained on data from the machines themselves, like temperature, vibration, sound, etc. As these models learn this data management, they can generate predictions about potential failures, allowing for preventative maintenance and reducing downtime.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

How Generative AI is Changing Industries

Yooz is an automated AI solution designed to assist accounting and finance leaders in managing invoices. The solution aims to streamline and automate the invoice processing workflow, reducing manual effort and enhancing overall efficiency. AI is providing a significant upgrade to banking operations by automating tasks that were previously performed manually, resulting in more efficient processes.

Generative AI raises ethical considerations such as the potential for deepfake creation, misinformation dissemination, and intellectual property infringement. Businesses using generative AI should establish clear guidelines and policies regarding the responsible use of such technology. Transparency, consent, and privacy should be prioritized to address these ethical concerns and ensure that generative AI is utilized in a responsible and trustworthy manner.

Most would agree that GPT and other transformer implementations are already living up to their name as researchers discover ways to apply them to industry, science, commerce, construction and medicine. Unscreen revolutionizes video editing by employing AI to automatically remove backgrounds from videos. With capabilities such as background removal, object painting, and subtitle addition, Runway makes professional-grade video editing accessible to everyone, regardless of their Yakov Livshits technical expertise. Notion AI delivers an enhanced user experience by providing an efficient and smooth workflow, earning the praise and appreciation of its user base. AGI, the ability of machines to match or exceed human intelligence and solve problems they never encountered during training, provokes vigorous debate and a mix of awe and dystopia. AI is certainly becoming more capable and is displaying sometimes surprising emergent behaviors that humans did not program.

As the name suggests, foundation models can be used as a base for AI systems that can perform multiple tasks. Today, developers and organizations are actively implementing this technology to create that lead to business transformation, innovation, growth, and better scalability. From creating and completing videos to expediting coding and enhancing chatbots, the generative AI use cases are continuously expanding. AlphaCode by DeepMind is one of the foremost problem-solving and coding solutions in the generative AI space.

#19 AI for content creation (text, images, video, audio…)

However, with GAN and generative AI, text-to-speech is much more natural and sounds like a human. Tools such as ChatGPT and others have shown their potential to write accurate codes for specific programs. This means professionals no longer have to worry about programs that must be added repeatedly; instead, generative AI can do that job. Image-to-image or image-to-photo translation is a method of converting a semantic sketch or image into a realistic one.

Generative solutions excel in sentiment analysis and extracting valuable insights from large datasets. Moreover, internal IT departments can utilize ChatGPT and other AI-driven solutions to assist them in code creation, issue fixing, and system testing. This approach enables saving resources, accelerating processes, and achieving results that surpass expectations. Given that we live in a digital era, having a generative AI tool that automates software production is one of the best ways to help a business thrive. They can engage with thousands of people simultaneously and even translate messages in real-time, eliminating language barriers. It’s a necessity for businesses looking to expand their reach and consistently provide the highest possible level of customer service.

This is especially beneficial for producing high-quality versions of archival or medical materials that are not cost-effective to save in high-resolution format. The technological landscape is in a constant state of evolution and is leading us toward a new industrial environment where humans are working with intelligent machines. These smart machines are embedded with various cognitive technologies such as artificial intelligence and machine learning. According to Gartner’s 2022 Emerging Technologies and Trends Impact Radar report, generative AI is considered a highly disruptive and rapidly advancing technology. Incredibly, the report predicts that generative AI will be responsible for generating 10% of all data (up from less than 1%) and 20% of test data for consumer applications by 2025.

Shutterstock Follows Adobe and Offers Legal Protection from Generative AI Copyright Claims

Now You Can Create Your Own AI Images with Shutterstock

It has a lot of unique features including changing the video background, editing effects in it, and much more. Furthermore, you can also use its AI tools to generate automated results in your videos. Shutterstock said that the AI Design Assistant has already been used by a test customer, which it described as a “global technology” firm, which used a generated image during a virtual developer conference in May. Getty Images is one of the largest stock photo sites in the world, and its decision to ban AI-generated content will likely have a ripple effect throughout the industry. Other stock photo sites may follow Getty Images’ lead in order to avoid any legal challenges that may arise from AI-generated content.

Powered by DALLE-2 and OpenAI, customers that use Shutterstock’s online design platform can quickly ask for AI-generated images in under 10 seconds. More importantly, whatever wild idea the artificial intelligence creates is “ready for licensing.” Adobe has made it clear it will pay out any claims for a lawsuit over Firefly content as well. Adobe provides a list of training sources, which includes its own library, collections licensed for training, or public domain databases. There’s also a new “Do Not Train” feature enabling artists to incorporate a request into an image’s metadata to block data scrapers from using it to train AI.

Q: What other collaborations has Shutterstock pursued in the field of generative AI?

“Our tools are built on an ethical approach and on a library of assets that represents the diverse world we live in, and we ensure that the artists whose works contributed to the development of these models are recognized and rewarded.” Shutterstock, the leading provider Yakov Livshits of stock content, has announced its plans to expand its existing deal with OpenAI, a prominent AI research and deployment company. This strategic partnership aims to fuel AI tech innovation by providing OpenAI with extensive training data for its AI models.

It’s worth highlighting here that, through the companies’ cooperation, Picasso’s 3D-generating model uses only fully licensed, rights-reserved data. The AI image generator, and the rest of our capabilities that are ready to turn your ideas into achievements, can be found on shutterstock.com. It’s a significant move Yakov Livshits — the first major initiative by a platform holder to reimburse creators in this way — but it also underscores the fraught legal and ethical questions surrounding this new technology. Picasso is part of NVIDIA AI Foundations, which advances enterprise-level generative AI for text, visual content and even biology.

Part 3: Alternatives of Creating The Perfect AI Images

Shutterstock deepens its involvement in creative AI tech as its first AI-focused virtual conference is announced. Shutterstock does require a small fee if you want to download the image without the watermark, but we believe it’s a small price to pay considering the recompensing of artists. Isa Muhammad is a writer and video game journalist covering many aspects of entertainment media including the film industry. He’s steadily writing his way to the sharp end of journalism and enjoys staying informed. If he’s not reading, playing video games or catching up on his favourite TV series, then he’s probably writing about them.

Let’s further explore Shutterstock’s indemnification offering and how they keep customers safe. Beyond these current capabilities, this technology will continue to boom in the future. Now, anyone can type out some thoughtful text, click a button and, within seconds, AI will generate a new image based off of what they wrote. Shutterstock brings fantasies to life through AI-generated art for New York Lottery’s new social campaign, #PictureAWin.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Shutterstock’s approach may also be a preview on how contributing photographers that inspire these AI models are compensated. The company said it would provide compensation for artists whose works have contributed to the development of AI models, as well as royalties whenever their intellectual property is used, but did not offer details beyond that. Shutterstock said it maintains an internal database of all assets that have been used to train AI models, so contributors can be compensated accordingly. Picsart AI is a tool that helps its users to edit and create images and videos quickly. With the integration of AI in it, the process has become more creative and easier.

• “If you don’t want your content to be a part of training new AI models, you’re able to opt out,” he said.
• Other stock photo sites may follow Getty Images’ lead in order to avoid any legal challenges that may arise from AI-generated content.
• Although scraping or buying data to train AI art generators seems to be legal (covered by Fair Use), many experts worry about future challenges and complications.
• Shutterstock said that its contributor fund funnels monetary compensation to the artists who created images that the AI Design Assistant was trained on.

Contributors to stock image galleries, including artists and photographers, have expressed concerns about generative AI startups profiting off their work without providing credit or compensation. To address this, Shutterstock maintains a “contributor fund” that pays artists for the role their work has played in training Shutterstock’s generative AI and ongoing royalties tied to licensing for newly generated assets. Shutterstock said that its contributor fund funnels monetary compensation to the artists who created images that the AI Design Assistant was trained on. “Our easy-to-use generative platform will transform the way people tell their stories — you no longer have to be a design expert or have access to a creative team to create exceptional work,” Hennessy said.

Shutterstock says that, in addition, OpenAI will work with it to bring generative AI capabilities to mobile users through Giphy, the GIF library Shutterstock recently acquired from Meta. Interestingly enough, many Shutterstock customers have already taken an interest in generative AI. In fact, 45% of Shutterstock customers have already used generative AI and 66% want to learn more about it. When applying this particular style, your resulting images will be rustic, rural, and cozy.

Once DALL-E is fully integrated into Shutterstock sometime next year, users will be able to use the tool to generate and customise their own images. The company says that it does not vet every AI image that customers create with its AI image generator tool but wants to reassure customers that the synthetic images they generate have the same protections as other types of content sold on its platform. One of Shutterstock’s main competitors, Getty Images, has said it wouldn’t be wading into the murky waters of AI anytime soon.

Most Underrated AI Startups in 2023: Ranked by Funding

This was in stark contrast to its competitor Getty Images, which sued Stability AI, the creator of the open-source text-to-image generator Stable Diffusion, for copyright infringement in February. Many of these artists are tapping generative AI to bolster their complex workflows — and will be able to use the technology to quickly create and customize environment maps. This allows more time to work on hero 3D assets, which are the primary assets of a 3D scene that viewers will focus on.

Furthermore, Peters stated that they’re collaborating with the C2PA (Coalition for Content Provenance and Authenticity) to build AI-based image filters. These filters will help Getty Images to keep its policies and guidelines up-to-date with the latest AI technologies. Today, the well-respected Getty Images disallowed AI images from being sold on its website and microstock agency, Stock.

However, for now, Getty and Shutterstock’s decision to avoid the risks involved in AI-created imagery is understandable. The move by Getty Images and iStock is sure to have a ripple effect throughout the industry. It will be interesting to see if other stock photo sites follow suit in banning AI-generated content. For now, it seems that Getty Images and iStock are leading the charge in protecting the authenticity of their images. Shutterstock’s indemnification offering is part of its strategic steps in bringing AI to creative production.

Deep Learning for NLP: Creating a Chatbot with Python & Keras!

5 Reasons Why Your Chatbot Needs Natural Language Processing by Mitul Makadia

Another future item will include programming languages for developing a chatbot. A chatbot is a piece of software or a computer program that mimics human interaction via voice or text exchanges. More users are using chatbot virtual assistants to complete basic activities or get a solution addressed in business-to-business (B2B) and business-to-consumer (B2C) settings. This might be a stage where you discover that a chatbot is not required, and just an email auto-responder would do.

Therapeutic chatbot that distributes the text into labels for emotions happiness, pleasure, shame, rage, disgust, sorrow, remorse, and Afraid. Also, based on the emotion mark, it identifies the users’ Mental state, such as overwhelmed or depressed by talking with users The chatbot is domain-specific whereby the engagement of users. The chatbot would seek to escape and recreate the depressive behavior [1]. Just kidding, I didn’t try that story/question combination, as many of the words included are not inside the vocabulary of our little answering machine. Also, he only knows how to say ‘yes’ and ‘no’, and does not usually give out any other answers.

Dual process: A Chatbot Architecture after ChatGPT

Some researchers have tried to artificially promote diversity through various objective functions. However, humans typically produce responses that are specific to the input and carry an intention. Because generative systems (and particularly open-domain systems) aren’t trained to have specific intentions they lack this kind of diversity. For new businesses that are looking to invest in a chatbot, this function will be able to kickstart your approach.

You’ve likely encountered NLP in voice-guided GPS apps, virtual assistants, speech-to-text note creation apps, and other chatbots that offer app support in your everyday life. In the business world, NLP is instrumental in streamlining processes, monitoring employee productivity, and enhancing sales and after-sales efficiency. For instance, customer care chatbots are created specifically to meet the needs of customers who request assistance, whereas conversational chatbots are created to engage in conversation with users. It is really possible to train with a large dataset and archive human level interaction but organizations have to rigorously test and check their chatbot before releasing into production. In the realm of chatbots, NLP comes into play to enable bots to understand and respond to user queries in human language. Well, Python, with its extensive array of libraries like NLTK (Natural Language Toolkit), SpaCy, and TextBlob, makes NLP tasks much more manageable.

nlp-chatbot

Simply put, machine learning allows the NLP algorithm to learn from every new conversation and thus improve itself autonomously through practice. The words AI, NLP, and ML (machine learning) are sometimes used almost interchangeably. NLP or Natural Language Processing has a number of subfields as conversation and speech are tough for computers to interpret and respond to. Speech Recognition works with methods and technologies to enable recognition and translation of human spoken languages into something that the computer or AI can understand and respond to. In the current world, computers are not just machines celebrated for their calculation powers. Today, the need of the hour is interactive and intelligent machines that can be used by all human beings alike.

With supervised training, chatbots give more appropriate responses instantly. After processing the human conversation through NLP, Natural language understanding converses with the customers by understanding the structure of the conversation. NLU breaks complex sentences into simpler ones to interpret human messages. As you can see, it is fairly easy to build a network using Keras, so lets get to it and use it to create our chatbot! The dataset has about 16 instances of intents, each having its own tag, context, patterns, and responses. If you thoroughly go through your dataset, you’ll understand that patterns are similar to the interactive statements that we expect from our users whereas responses are the replies to those statements.

They understand and interpret natural language inputs, enabling them to respond and assist with customer support or information retrieval tasks. After all of the functions that we have added to our chatbot, it can now use speech recognition techniques to respond to speech cues and reply with predetermined responses. However, our chatbot is still not very intelligent in terms of responding to anything that is not predetermined or preset.

The important aspect is that these systems are good at comparing a fixed set of rules. Generate leads and satisfy customers

Chatbots can help with sales lead generation and improve conversion rates. For example, a customer browsing a website for a product or service may need have questions about different features, attributes or plans. A chatbot can provide these answers in situ, helping to progress the customer toward purchase. For more complex purchases with a multistep sales funnel, a chatbot can ask lead qualification questions and even connect the customer directly with a trained sales agent.

With its intelligence, the key feature of the NLP chatbot is that one can ask questions in different ways rather than just using the keywords offered by the chatbot. Companies can train their AI-powered chatbot to understand a range of questions. For the training, companies use queries received from customers in previous conversations or call centre logs. NLP chatbot is an AI-powered chatbot that enables humans to have natural conversations with a machine and get the results they are looking for in as few steps as possible. This type of chatbot uses natural language processing techniques to make conversations human-like.

• An NLP chatbot is a more precise way of describing an artificial intelligence chatbot, but it can help us understand why chatbots powered by AI are important and how they work.
• Most of the time, neural network structures are more complex than just the standard input-hidden layer-output.
• Apps such as voice assistants and NLP-based chatbots can then use these language rules to process and generate utterances of a conversation.
• We are going to build a chatbot using deep learning techniques following the retrieval-based concept.

Read more about https://www.metadialog.com/ here.

Generative AI: What Is It, Tools, Models, Applications and Use Cases

Top 100+ Generative AI Applications Use Cases in 2023

Generative AI can improve the quality of outdated or low-quality learning materials, such as historical documents, photographs, and films. By using AI to enhance the resolution of these materials, they can be brought up to modern standards and be more engaging for students who are used to high-quality media. It can allow students to interact with a virtual tutor and receive real-time feedback in the comfort of their home. This makes it an ideal solution for those children who may not have access to traditional face-to-face education. By leveraging generative AI, personalized lesson plans can provide students with the most effective and tailored education possible.

They enable the generation of realistic images, art synthesis, and interactive exploration of latent spaces. It can help people who work in art, fashion, or product design create new and exciting content. A generative AI model is a neural network combining specific neurons in a specific way to generate new content based on the data fed as input. The explosive growth of generative AI shows no sign of abating, and as more businesses embrace digitization and automation, generative AI looks set to play a central role in the future of industry. The capabilities of generative AI have already proven valuable in areas such as content creation, software development and medicine, and as the technology continues to evolve, its applications and use cases expand. It’s a large language model that uses transformer architecture — specifically, the generative pretrained transformer, hence GPT — to understand and generate human-like text.

Generative AI models

At its core, generative AI is a subset of artificial intelligence that excels at creating something new from existing data. Whether it’s crafting sentences, composing music, or generating realistic images, this technology is reshaping the landscape of creativity and utility. Transformers are a type of machine learning model that makes it possible for AI models to process and form Yakov Livshits an understanding of natural language. Transformers allow models to draw minute connections between the billions of pages of text they have been trained on, resulting in more accurate and complex outputs. Without transformers, we would not have any of the generative pre-trained transformer, or GPT, models developed by OpenAI, Bing’s new chat feature or Google’s Bard chatbot.

These algorithms can analyze large amounts of data in real time, allowing businesses to quickly respond to changing consumer trends and market conditions. This is particularly important in the e-commerce industry, where companies need to be able to react quickly to customer demands and changes in the market. This program offers a thorough grasp of AI concepts, machine learning algorithms, and real-world applications as the curriculum is chosen by Yakov Livshits industry professionals and taught through a flexible online platform. By enrolling in this program, people may progress in their careers, take advantage of enticing possibilities across many sectors, and contribute to cutting-edge developments in AI and machine learning. One of the breakthroughs with generative AI models is the ability to leverage different learning approaches, including unsupervised or semi-supervised learning for training.

Reinforcement Learning for Generative Tasks: Security & Privacy Use

On top of it, generative AI tools also offer the benefits of training with natural language processing and neural networks. As a result, generative AI could help in making more sense of input data for offering desired outputs to users. Generative AI models could rely on training with massive volumes of relevant, unbiased, and ethical training data to achieve better efficiency. In the realm of artificial intelligence (AI), generative models have emerged as powerful tools capable of creating new and imaginative content. By leveraging sophisticated algorithms and deep learning techniques, these models enable machines to generate realistic images, texts, music, and even videos that mimic human creativity. In this article, we will delve into the world of AI generative models, exploring their definition, purpose, applications, and the key concepts that drive their success.

To be part of this incredibly exciting era of AI, join our diverse team of data scientists and AI experts—and start revolutionizing what’s possible for business and society. Generative AI can help with face identification and verification systems at airports. By creating a full-face picture of a passenger from photos taken from different angles, the technology can make it easier to identify and verify the identity of travelers. Generative AI can accurately convert satellite images into map views, enabling the exploration of previously unknown locations. This can be especially useful for logistics and transportation companies looking to navigate new areas. If we build a product, we want to be confident it can be helpful and avoid harm.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

These models are capable of generating new content without any human instructions. In simple words, It generally involves training AI models to understand different patterns and structures within existing data and using that to generate new original data. In conclusion, AI generative models have revolutionized content creation and innovation by enabling machines to generate realistic images, texts, music, and videos. Through VAEs, GANs, auto-regressive models, and flow-based models, AI generative models have opened doors to new possibilities in art, design, storytelling, and entertainment.

• As an example, a protein classification tool would operate on a discriminative model, while a protein generator would run on a generative AI model.
• To achieve this, it employs complex algorithms to understand the rules, structures, and patterns within existing data.
• Visual
  Generative AI’s impact shines in the visual realm, creating 3D images, avatars, videos, graphs, and more.

As the name suggests, Generative AI means a type of AI technology that can generate new content based on the data it has been trained on. Generative AI can produce a wide Yakov Livshits range of outputs based on user input or what we call “prompts“. Generative AI is basically a subfield of machine learning that can create new data from a given dataset.

Generative AI can be used to generate synthetic customer profiles that help in developing and testing models for customer segmentation, behavior prediction, and personalized marketing without breaching privacy norms. Generative AI can help forecast demand for products, generating predictions based on historical sales data, trends, seasonality, and other factors. This can improve inventory management, reducing instances of overstock or stockouts. Generative AI can create realistic and dynamic NPC behavior, such as enemy AI and NPC interactions. This can help game developers to create more immersive and challenging game worlds. Generative AI can generate game content, such as levels, maps, and quests, based on predefined rules and criteria.

We train these models on large volumes of text so they better understand what word is likely to come next. One way — but not the only way — to improve a language model is by giving it more “reading” — or training it on more data — kind of like how we learn from the materials we study. We call machines programmed to learn from examples “neural networks.” One main way they learn is by being given lots of examples to learn from, like being told what’s in an image — we call this classification.

Subsequent research into LLMs from Open AI and Google ignited the recent enthusiasm that has evolved into tools like ChatGPT, Google Bard and Dall-E. Design tools will seamlessly embed more useful recommendations directly into workflows. Training tools will be able to automatically identify best practices in one part of the organization to help train others more efficiently.