Generative AI Could Add $4.4 Trillion a Year to the Global Economy, McKinsey Finds

 Generative AI Could Add $4.4 Trillion a Year to the Global Economy, McKinsey Finds

Vanessa Bates Ramirez in SigularityHub

There’s been concern about artificial intelligence taking away jobs for years, and with the recent boom in generative AI, those fears have grown. The ability to generate realistic and accurate text, images, or audio based on a prompt could make plenty of jobs obsolete (including, ahem, journalism and writing). But a new study says the doomsday predictions are misguided, because generative AI is far more likely to do just the opposite of canceling out jobs.

Last week, McKinsey published a report called The Economic Potential of Generative AI: The Next Productivity Frontier. It’s the result of a study involving 850 different job roles and 2,100 tasks across occupations in 47 countries. Researchers considered what portion of each existing job or task can be taken over by generative AI, as well as new occupations and responsibilities likely to be created by the technology. Their conclusion? Generative AI has the potential to create up to $4.4 trillion worth of annual value in the global economy.

$4.4 trillion is the high end of a range, with the lower bound sitting at $2.6 trillion. Even if the value created were to fall on the low end, it would still approximate the GDP of the United Kingdom, which was $3.1 trillion in 2021.  ... ' 

Generative AI at Mayo

AI in Healthcare at Mayo Clinic.  A good example for an example of data workflow.  

Mayo Clinic Partners With Google Cloud to Bring Generative AI to Healthcare

ERIC HAL SCHWARTZ on June 8, 2023 at 8:00 am

The Mayo Clinic is working on enhancing its work using generative AI as part of a new collaboration with Google. The healthcare center’s first project aims to speed up its research by integrating Google Cloud’s Enterprise Search in Generative AI App Builder within its clinical workflows. In tandem with the Mayo Clinic deal, Google Cloud announced that the Gen App Builder can now create systems capable of complying with HIPAA.

GENERATIVE MAYO

Google’s Gen App Builder offers an enterprise search feature useful for collating and finding data from a wide range of sources. Since doctors and medical professionals must look through patient records, research papers, hospital protocols, and other databases to plan any treatment or diagnosis plan, an AI assistant that can streamline the process has an obvious appeal. The HIPAA compliance Google Cloud’s platform now supports is crucial for the Mayo Clinic or any other healthcare provider, however, as they would want to ensure data security and privacy before they start linking generative AI products to patient records.

“Our prioritization of patient safety, privacy, and ethical considerations, means that generative AI can have a significant and positive impact on how we work and deliver healthcare,” Mayo Clinic chief information officer Cris Ross explained. “Google Cloud’s tools have the potential to unlock sources of information that typically aren’t searchable in a conventional manner, or are difficult to access or interpret, from a patient’s complex medical history to their imaging, genomics, and labs. Accessing insights more quickly and easily could drive more cures, create more connections with patients, and transform healthcare.”  .. ' 

AI Being Used

Its here, AI being being used by McKinsey

 McKinsey says ‘about half’ of its employees are using generative AI   By Carl Franzen @carlfranzen,  June 6, 2023 9:45 AM

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More

McKinsey and Company, the global consulting firm with more than 30,000 employees in 67 countries, is embracing new generative AI tools in a major way: Nearly 50% of the firm’s workforce is using ChatGPT and similar technology.

“About half of [our employees] are using those services with McKinsey’s permission,” said Ben Ellencweig, senior partner and global leader of QuantumBlack, the firm’s artificial intelligence consulting arm, during a media event at McKinsey’s New York Experience Studio on Tuesday.

Ellencweig emphasized that McKinsey had guardrails for employees using generative AI, including “guidelines and principles” about what information the workers could input into these services. ...'

Will Generative AI Improve Customer Experiences in Retail

 Useful thoughts and directions.

Will Generative AI Improve Customer Experiences in Retail  by Patricia Vekich Waldron

Customer experience (CX) and customer service leaders gathered in San Diego this week for the Reuters Events: Customer Service & Experience West 2023 conference. The consensus was that customer care is a brand differentiator and strategic focus for companies–especially given that customer expectations, call volumes, service employee attrition and training costs are all up.

In a session titled “Generative AI in CX,” Shawn Strassburg, head of transformation, Best Buy, discussed how the consumer electronics retailer uses artificial intelligence to solve specific CX/contact center business problems.

“Three and a half years ago, where the company made a very on-purpose decision, that we were going to re-envision customer care with a key focus on customer obsession. And really, really understanding your customers’ words, their voice,” said Mr. Strassburg. The retailer is halfway through its transformation.

The company uses a top-down/bottom-up approach to understand customers using data to guide decision-making. Leadership, business, data and analytics, and finance teams secured a top-down commitment and alignment to truly understand customers and the internal customer care processes. Bottom-up insights come from front-line teams, call center agents’ experiences, and customers’ experiences interacting with Best Buy’s CX organization.

Best Buy created a lab staffed with top agents and discovered a disconnect between contact center system data and what was happening as customers called in. To improve agent performance and customer experience, Best Buy implemented AI for real-time audio transcription, automated contact summaries and contact intent detection.

Lab trials have delivered a 25 percent reduction in after-call work (ACW) as call agents no longer must manually compose and enter call history and actions. There was also an 18-second reduction in talk time.

“As we tested it out, we were anticipating about a 15-second reduction in just talk time, not a negative way, but in a super positive way because it was helping to build a foundation of trust and partnership within our agent base and in our customer base,” said Mr. Strassburg.

Best Buy is auditing its systems and processes to identify where to plug in AI capabilities to hear the voice of their customer better, discover the true intent of why they call for service and determine if it has succeeded in resolving issues.

DISCUSSION QUESTIONS: What role(s) do you see generative artificial intelligence playing for retailers looking to improve the customer experience? How does Best Buy’s approach compare to other retailers? ... ' 

How Generative AI is Changing the Knowledge Paradigm for Enterprises

Good thoughts

From VB Spotlight

How generative AI is changing the knowledge paradigm for enterprises

VB Staff, May 12, 2023 5:05 AM

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More   Presented by Glean

At the enterprise level, keeping track of internal data and information has become an enormous challenge. In this VB Spotlight event, learn how new generative AI experiences are unlocking the full potential of data in enterprise environments and reducing time to knowledge.

With the increasing complexity and distributed nature of organizations – far-flung teams, remote work, and a multitude of knowledge systems, data is difficult to track down across an entire enterprise knowledge ecosystem, and workers are feeling the toll.

Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.

This knowledge access challenge “results in a loss of productivity and a frustration that we’re starting to see, leading to diminishing engagement from our employees,” says Phu Nguyen, head of digital workplace at Pure Storage during the recent VB Spotlight, “The impact of generative AI on enterprise search: A game-changer for businesses.

He was joined by Jean-Claude Monney, a digital workplace, technology and knowledge management advisor and Eddie Zhou, founding engineer, intelligence at Glean to discuss the emergence of the evolutionary leap forward in workplace-specific search tools, powered by generative AI, that gives employees full access to the knowledge they need, and its context, anywhere in the organization.

The evolution of enterprise search

Traditional enterprise search can’t reach all the knowledge in an organization, which is spread out in multiple systems. It can mine structured knowledge, such as the data found in Jira, Confluence, intranets and sales portals, but unstructured knowledge, the information communicated through IM, Teams, Slack, and email, has been uncharted territory, difficult to corral in any helpful contextual way, Nguyen adds.

“The paradigm of knowledge management has changed significantly,” he says. “How do you have a system that can look at both structured and unstructured data and provide you with the answers that you’re ultimately looking for? Not the information that you need, but the answer that you’re looking for.”

Solutions that integrate with multiple systems and utilize generative AI can address these challenges, and help employees find the information they need to perform their jobs effectively, no matter where that knowledge resides.

“Companies are now building searches specifically for the workplace, built for internal searches that work across your internal system,” Nguyen explains. “Most importantly, they’re built on a knowledge graph that returns a search that’s more relevant to your employees. This is all very exciting for us because we think of this as part of our employee information center strategy. Previously it was just an intranet and our support portal, but now we have this workplace search that can connect information across multiple systems inside our organization.”  ... ' 

Intro to Leveraging Generative AI Chat in Customer and Employee Experiemce

 Good introductory non-technical piece. 

How Business Leaders Can Leverage Generative AI in Customer and Employee Experience  By Thomas Wieberneit -March 1, 2023   in Customerthink

The AI playground has evolved a lot throughout the past year. Conversational AI has made huge inroads. We have seen an increasing number of deployments and through their availability, people get to use them more and more, such as Apple’s Siri, Amazon’s Alexa, or Google’s Assistant.

I see it in my own family; we are increasingly asking Siri for something instead of opening a browser and typing our search query.  At the same time, the little windows of chatbots have become quite ubiquitous on websites.

And then, the next iteration of AI hype had arrived: Generative AI. We were introduced to systems like Dall-E or chatGPT of Open AI or You.com that also offers search, to name only a few of the best-known ones. In the meanwhile, Microsoft has also integrated chatGPT into Bing, while Google has suffered from the probably most expensive demo ever when its generative AI gave a wrong answer. Apps like Lensa popped up after ones like SnapChat offered AI-based filters for some time.

Of course, I ventured and tried services, generated images for some of my blog posts, got into some “discussion” about web3, even had it generate a sample terms and conditions document for a consulting agency. Guess what, it came with a lot of disclaimers.

AP is using generative AI for some years now while CNN seems to have overdone it in a recent experiment. Google Duplex got deprioritised some four years ago as it was a bit too good, i.e. creepy.

Some people already ventured to say that generative AI will supersede conversational AI. Will this really be the case, or is this only the next craze?

This leads to an obvious initial question.

What is a conversational AI vs. a generative AI?

I mean, apart from conversational AI not being able to create pictures.

Who better to ask than ChatGPT, You.com, and Google? This also gives a good chance to see how easy it is to get an answer to the question: “What is the difference between generative AI and conversational AI?”

Google, of course, provides several well-fitting links that I can use to figure it out myself. No surprise here.

ChatGPT replied as follows:

“Generative AI and conversational AI are both subfields of artificial intelligence, but they have different applications and focus on different tasks.

Generative AI refers to the branch of AI that focuses on creating new content or data. This can include creating images, music, text, and more. These models are trained on large datasets and use statistical patterns to generate new, unique content that is similar to the training data. Examples of generative AI include GPT-3 (which I am based on) and other language models, and Generative Adversarial Networks (GANs) used in image and video generation.

Conversational AI, on the other hand, is focused on creating systems that can understand and respond to natural language input from users. These systems can include chatbots, voice assistants, and other interfaces that use natural language processing (NLP) and machine learning to understand and respond to user requests. Examples of conversational AI include Apple’s Siri, Amazon’s Alexa, and Google Assistant.

In summary, Generative AI generates new and unique content while conversational AI focuses on understanding and responding to natural language input from users.”

The ‘You Write’ service of You.com made the following blog style statement for professionals in a neutral tone.

AI technology has come a long way in the past few years, and with it have come two distinct types of AI: generative AI and conversational AI. While both are powerful tools that can be used to automate tasks or provide intelligent services, they do so in different ways.

Generative AI is focused on creating new content from existing data sets. It uses algorithms to generate patterns based on what it already knows about a given topic or dataset. This type of artificial intelligence can be used for things like natural language processing (NLP) applications such as summarization, translation, question-answering systems, and more. Generative models are also commonly used for image recognition tasks such as facial recognition software and self-driving cars which use deep learning techniques to recognize objects around them in real-time scenarios..

Conversational AI focuses more on understanding user intent through natural language interaction by using speech-to-text technologies like voice assistants (e.g., Alexa), chatbots, or virtual agents that interact with customers over text messages via SMS or messaging apps like Facebook Messenger, etc. Conversational AIs use NLP techniques similar to those found in generative models but instead focus solely on responding accurately based on input given by users rather than generating new content from scratch. These bots usually rely heavily upon predictive analytics methods such as machine learning algorithms which allow them to learn how best to respond when presented with certain inputs/questions over time. Generally speaking, this type of artificial intelligence is designed specifically for customer service purposes where accurate responses need quickly provided within short periods without any human intervention necessary.

In conclusion, while both Generative AIs and Conversational AIs share similarities due to their reliance upon Natural Language Processing techniques, they each serve a unique purpose depending upon the task at hand; one being able to create completely original pieces of work while other providing quick automated responses within specific contexts respectively.”

Asked for a paragraph in a neutral tone for a professional audience You.com gave a shorter summary.

“Generative AI and Conversational AI are two distinct types of Artificial Intelligence (AI). Generative AI is focused on creating new content, such as images or text, while conversational AI focuses on understanding natural language. Generative models create data that can be used to train other machine learning models, while conversational models use speech recognition technology to interpret human conversations. To write well with either type of Artificial Intelligence requires an understanding of the strengths and weaknesses associated with each model. Additionally, it is important to consider how best to communicate your message in a way that resonates with both humans and machines alike.”

In summary, both use machine learning, in particular deep learning. Both need some ability of natural language processing. Conversational AI has text/speech output whereas generative AI can also create images or music. Conversational AI focuses on understanding user intent and to react on it, generative ai is about creating an output based on what it was trained on.

And then there are some major differences: Generative AI is using some “world knowledge” that is provided by large language models to generate results, but it is not connected to any business system. A conversational AI is; it is also an orchestration layer. Where generative AI creates human-like responses, conventional chatbots as delivered by current conversational AI are somewhat awkward without putting very high effort into them. Generative AI also sometimes tends to create some fiction — with utter confidence. This is to quite an extent the result of them not being task-bound like conversational Ais. They do not know about any business, which means that their knowledge needs to be augmented by business-specific knowledge.  ....  (more) 

OpenAI Blooms

Worth a look, still have my doubts except for specialized applications.     We did much in this area I would like to re-try with ChatGBT,  Worth the experiment.  Data and buy in for customer experiments now less available. 

 OpenAI Booms, Even Amid Tech Gloom

The New York Times

Erin Griffith; Cade Metz, January 7, 2023

OpenAI, whose ChatGPT has seen over 1 million users since its release, is in talks to complete a potential deal in which $300 million in existing company shares would be sold in a tender offer. Amid the hype over generative artificial intelligence (AI) tools like ChatGPT, sources say the deal would value OpenAI at about $29 billion, more than double its 2021 valuation. Despite a dismal year for the tech industry in 2022 that involved mass layoffs and other cuts, tech investors are excited about generative AI. PitchBook reported generative AI companies received at least $1.37 billion from investors in 78 deals last year, an amount nearly equal to investments made in the previous five years combined. ...  ' 

Generative AI and Games

Intriguing approach .

The Generative AI Revolution in Games

Excerpt from James Gwertzman and Jack Soslow   in a16z.com,  Andreessen Horowitz

AI, machine & deep learning  gaming, social, and new media  Generative AI  machine learning

To understand how radically gaming is about to be transformed by Generative AI, look no further than this recent Twitter post by @emmanuel_2m. In this post he explores using Stable Diffusion + Dreambooth, popular 2D Generative AI models, to generate images of potions for a hypothetical game.

What’s transformative about this work is not just that it saves time and money while also delivering quality – thus smashing the classic “you can only have two of cost, quality, or speed” triangle. Artists are now creating high-quality images in a matter of hours that would otherwise take weeks to generate by hand. What’s truly transformative is that:

This creative power is now available to anyone who can learn a few simple tools.

These tools can create an endless number of variations in a highly iterative way.

Once trained, the process is real-time – results are available near instantaneously.

There hasn’t been a technology this revolutionary for gaming since real-time 3D. Spend any time at all talking to game creators, and the sense of excitement and wonder is palpable. So where is this technology going? And how will it transform gaming? First, though, let’s review what is Generative AI?

Generative AI is a category of machine learning where computers can generate original new content in response to prompts from the user. Today text and images are the most mature applications of this technology, but there is work underway in virtually every creative domain, from animation, to sound effects, to music, to even creating virtual characters with fully fleshed out personalities.

AI is nothing new in games, of course. Even early games, like Atari’s Pong, had computer-controlled opponents to challenge the player. These virtual foes, however, were not running AI as we know it today. They were simply scripted procedures crafted by game designers. They simulated an artificially intelligent opponent, but they couldn’t learn, and they were only as good as the programmers who built them.

What’s different now is the amount of computing power available, thanks to faster microprocessors and the cloud. With this power, it’s possible to build large neural networks that can identify patterns and representations in highly complex domains.

This blog post has two parts:

Part I consists of our observations and predictions for the field of Generative AI for games. ... '