For decades, writers and filmmakers have imagined dystopian futures where AI displaces and tries to destroy humanity. So, it’s understandable that people are apprehensive about generative AI’s foray into the workplace. It’s important to acknowledge AI’s transformative effect on workplaces, industries, economies and everyday life in ways we’re just starting to envision.

Even before ChatGPT came on the scene, researchers projected AI would displace 85 million jobs by 2025, but that same study also predicted 97 million new jobs will be created. Organizations that embrace and develop AI, provide incentives for employees to use it and create a human-centric culture where AI helps employees succeed at work will win the future.

As CTO of a team that recently launched a successful AI product for the global employer of record market, I’m optimistic — not only about the value of the technology but also for the employees whose knowledge and expertise AI will augment. Here’s a closer look at how employees and AI complement each other’s strengths and can work together to create business value within a human-centric company culture.

AI Should Augment Human Capabilities Rather Than Replace People

To address the elephant in the room, AI won’t replace human knowledge workers. It requires our insight into what’s happening in the real world to grow and learn to accomplish new tasks. According to some studies, approximately 90% of content online is predicted to be AI generated by 2026, i.e., synthetic data. In some scenarios, synthetic data is useful for training specific models. In fact, a 2021 leveraging synthetic data to refine AI-powered fraud detection tools.

But past a certain point, synthetic data can corrupt large language models due to LLMs depending on unique, creative insight related to real-life events, systems and trends to produce reliable answers. Models like ChatGPT may curate and repurpose existing content, but they also rely on human knowledge and creative thinking that transcends linear reasoning. For example, if companies are trying to hire, recruit and pay international talent, turn to AI for the latest HR best practices, which often change in response to new legislation and emerging trends, they’d need to use an AI model that incorporates human expertise to ensure accurate answers.

Some of the most exciting applications of generative AI are solutions that combine proprietary business expertise with LLM platforms to create a generative AI chat interface. This type of knowledge base can expedite delivery of information to internal and external customers and become a more valuable business asset over time as it learns and improves. But that’s only the case if company leaders think of employees not just as people who help administer the technology but as partners in a sociotechnical system, where people and AI work together, each making contributions to generate value.

Redefining Employee Roles in AI-Powered Industries

The best way to create a system where interactions between humans and technology create value is to build AI solutions in collaboration with employees, not as a separate project to replace them. This is especially important in industries that offer expertise and knowledge as their primary product.

A productive AI-human partnership will require new ways of thinking about how to deliver knowledge to customers. For example, in some jobs, employees apply expertise by responding to emails, submitting helpdesk tickets or collaborating with customers on phone calls. A new role for those employees might be analyzing emerging trends, writing content and training models, bringing the same expertise to solve customer problems in a novel way.

To develop AI solutions that augment employee expertise and knowledge, technology leaders will first need unfettered access to company data — including information that might be difficult to reach due to the use of legacy systems. They’ll also require AI-specific talent to help build out the solution, a blend of expertise from the software engineering and data science disciplines. That’s a commodity already in short supply, so finding people with the right skills should be a priority for an AI project.

Equally as important, building AI solutions will require incentives across the workforce to fine-tune the generative AI solution after launch. As the model ingests more and more information, it learns and becomes more adept at solving problems. Employers who bring more people into the conversation with AI will create additional value by providing multiple perspectives. So, if a customer has a question, the AI chat interface can provide an initial answer that can later be refined by human partners to create a solution that includes well thought-out strategies.

Building a Human-Centric Culture

So how can companies create a generative loop where humans and AI work together to constantly improve output? My AI project team created a chat interface that automates some of the mundane and time-consuming aspects of our knowledge workers’ jobs, and this has proved to be a powerful motivator for employees to keep engaging with the system. It frees staff from tedious tasks and gives them more time to focus on higher level work. That benefit answers the “what’s in it for me?” question that employees naturally ask themselves.

Other real-world examples of ROI from our AI project include employees getting instant answers to questions, when before they used to have to wait hours or days for a subject matter expert to weigh in so they could resolve an issue for a client. Another internal user called the chat interface a gamechanger because it can instantly access data across multiple systems that a person would have to laboriously assemble by consulting several different applications and datasets.

If you can create an architecture that seamlessly accesses data companywide and put together an innovation team that’s encouraged to experiment and explore new possibilities with emerging AI technology, you can build a solution that adds value immediately. And if you operate within an environment where people are adaptable and motivated to access AI to fulfill your company’s mission, you can leverage that human-centric culture to transform the business.

Every business will adapt in its own unique way, but being transparent about how AI will affect roles, encouraging employees to embrace change and instituting a more collaborative approach are essential across the board. Ready or not, generative AI will continue to transform the way companies operate, and an AI-driven business transformation, that leverages human capital, can offer exciting opportunities for businesses to serve customers better and gain a competitive edge.

Duri Chitayat is CTO of Safeguard Global

The post Leading the Evolution Towards Human-Centric AI Work Cultures appeared first on My TechDecisions.

Artificial Intelligence (AI) is quickly becoming a mainstay in our everyday lives. Whether it be text-to-image generators like DALL-E or language processing tools like ChatGPT, sophisticated AI tools are revolutionizing the way we work.

That’s especially true in the hybrid workplace, where organizations are searching for new ways to bridge the gap between distributed employees. Given this challenge, it’s no wonder that videoconferencing has emerged as AI’s next step in revolutionizing how we live and work.

Let’s explore AI’s role in videoconferencing and how its latest innovations are improving the meeting experience.

The Rise of AI-Powered Videoconferencing

It should come as no surprise that demand for videoconferencing apps has grown exponentially since the start of the pandemic in spring 2020. Without question, virtual meetings are the bedrock of hybrid work, enabling organizations to seamlessly connect and collaborate across a distributed workforce.

This uptick in demand runs parallel with the emergence of several new competitors in the videoconferencing market. In turn, vendors have been compelled to improve their services by releasing feature-rich additions to their existing applications.

AI technologies have also advanced in leaps and bounds over the same period. In fact, reports show that AI adoption is 2.5 times greater now than it was in 2017. The market is projected to skyrocket twentyfold by 2030 to nearly $2 trillion.

Now, the two worlds are converging. Leading platforms are weaving AI capabilities into their videoconferencing tools to enhance the meeting experience for maximum productivity and performance.

Four Ways AI Can Transform the Videoconferencing Experience

Gone are the days when organizing, running and managing your virtual meetings was a clunky, cumbersome endeavor. Thanks to AI’s advantages, videoconferencing platforms are empowering organizations to kick up their internal and external meetings several notches.

Here are four of the most impactful ways AI-driven solutions can improve the meeting experience.

#1 Natural Language Processing (NLP)

Natural language processing is a branch of AI that enables computers to understand text and spoken words in the same way a real person can. In other words, it’s about giving AI programs the power of conversational intelligence.

Modern videoconferencing tools are leveraging NLP across a host of innovative capabilities, including:

• Voice-to-text transcription: Using NLP, an AI-enabled platform can recognize and process complete sentences as they’re spoken during the meeting. This allows the system to automatically capture meeting notes, summarize conversations and generate searchable transcripts. When machine learning tools are incorporated, videoconferencing platforms can even identify who is speaking during the meeting.
• Real-time translation: Global enterprises may be holding internal or external meetings that stretch beyond borders and include attendees who speak different languages. With AI, the meeting software can give everyone an equitable experience by automatically translating live audio into the language of any user’s choice.
• Closed captions: Likewise, NLP capabilities can analyze audio and generate closed captions to make the meeting experience more inclusive for all participants.
• Noise removal: From cars and pets to housemates and more, there’s no telling what sounds might suddenly disrupt and derail a meeting. Machine learning can be used to train AI to identify and remove those noises before they’re picked up. For hybrid teams, this is especially useful, as automatic background noise removal and acoustic fencing filters out distractions in different locations to keep meetings running smoothly.
• Sentiment analysis: Consider the customer experience use case for videoconferencing: contact center agents working on resolutions in real-time. One major benefit for agents is AI’s ability to understand sentiment and tone during a conversation. Programs can analyze a speaker’s voice to uncover pain points and frustrations during the interaction. This allows managers to evaluate both customer experience and agent performance for future improvements.

#2 Conversational AI

Although they’re also built on NLP, conversational AI programs stand on their own. In simple terms, conversational AI refers to platforms that can chat and interact with users. That said, their capabilities stretch far beyond simple back-and-forth communication.

For example, voice-based virtual assistants can be used to streamline meeting management. Users can simply speak to their endpoints and apps to kickstart a meeting, turn on their camera, capture important items and control the experience from start to finish.

Chatbots can also be asked to schedule follow-up meetings with attendees, assign action items, send automated reminders and more.

#3 Computer Vision (CV)

According to Aragon Research, computer vision will have the biggest impact on intelligent videoconferencing in the future. This type of AI uses algorithms to understand, analyze and reconstruct visual inputs from image or video data.

This has several innovative use cases for meetings:

• Optimized framing: CV can help participants stay in their cameras’ frame when presenting to colleagues and customers. By automatically tracking the speaker’s movements, the system keeps you centered in your video to eliminate unwanted distractions. In addition, multiple cameras in a room can now work together by switching cameras views to focus on the active speaker. The result? A cinematic video experience that follows the conversation from one speaker to the next.
• Adaptive tracking: For meetings with multiple speakers, CV programs can ensure everyone is equally represented on screen. During webinars, cameras can adaptively track speakers as they move across the room or auditorium stage, ensuring that they’re always in everyone’s view.
• Immersive presentations: Virtual presentations are challenging in hybrid settings. CV allows presenters to deliver more immersive content by automatically screen sharing in front of presentation materials.
• Meeting zones: AI will soon use machine learning to automatically learn and frame your space based on predefined boundaries. This will ensure that distraction from passers-by are removed from the meeting in glass-walled rooms or open space.

#4 Productivity Analytics

People spend a lot of time in meetings these days. So, it’s important for organizations to ensure that time is well spent on value-added activities and core business processes. Fortunately, AI-powered solutions are helping businesses capture meeting data and leverage it to their advantage.

AI enhances data collection and offers users the power to evaluate key metrics. For example, platforms can record time spent on certain agenda items and activities, back-to-back meetings and other real-time insights that can be delivered directly to employees to help improve their productivity.

The Future of AI-Driven Videoconferencing

In truth, we are only scratching the surface when it comes to what AI can bring to the table. But, with the videoconferencing market expected to double by 2030, it’s safe to say that AI-powered meetings will be a major asset for years to come.

Robyn Rawlings is director of campaigns and content marketing at Webex by Cisco.

The post AI’s Revolutionary Impact on the Videoconferencing Experience appeared first on My TechDecisions.

Technology has been a passion of mine for a long time, so I have personally found it fascinating to watch the variety of opinions and perspectives unfold as technologies mature. It has challenged my thinking and preconceptions, and I recognize that as a leader and a human I need to address them.

I’ve covered everything from fundamental ethics and whether it is good or bad, through to more specific considerations like ‘what do I want from AI?’, and therefore ‘what might others want from it?’.

Naturally, it’s been hotly debated by my colleagues. The leadership team is considering how today’s and tomorrow’s versions of AI should shape our own role in AI creation and adoption.

We already know that artificial intelligence for IT operations (AIOps) is poised to take advantage of the benefits. At Avantra, we’ve long evangelized the value of automation to offset risk, better utilize skill and boost productivity and innovation. But the debate that surrounds AI has helped us understand that our next phase of technical development must be underpinned with even greater pragmatism and responsibility.

The latest numbers I saw suggest that every day, 100 million people are experimenting with the likes of ChatGPT and other Large Language Models (LLM), such as Bard. Numbers like this highlight the popularity and allure that machines still have. Just like the washing machine, if it makes life easier, why would you not use it?

Download: ChatGPT and Generative AI in the Workplace

Proceeding with Caution

However, even though world renowned university colleges are condoning the use of conversational AI tools, leading experts are urging caution — legalities, politics, economics and ethics are top of the list of concerns.

It was eloquently summed up by The Future Life Institute, which is made up of over 1,000 experts, in an open letter asking the industry to pause AI development, or risk humanity and society. LLMs are learning so much so fast, that we, as a species, haven’t had time to truly process the long term impact. Ethics are at stake.

Taking Responsibility is Urgent

I think it’s a responsible challenge. As I said before the headlines have provoked my own thinking to evolve and prompted me to consider whether such a warning could, should or even will stop our own industry from forging on.

This is where I think the application of AI must be balanced against the dilemma. Take the example of producing project documentation or new product technical summaries. Is using ChatGPT to create the first draft irresponsible or a boon for productivity, freeing up time for innovation in other areas? Similarly, with the introduction of ‘copilot’ tools, like Microsoft assistant, people can increase their productivity and have more time for other things, even just going to the gym. I can see how it could make a sustainable argument for a four day week and happier colleagues.

Can Conversational AI Help Our Industry?

Of course, in my world, the real advantage of introducing ML and AI is the ability to help customers find answers to the problems they face. Using conversational AI to mine a database of known and defined errors other businesses have encountered — be that on SAP or Google — would help practitioners arrive at answers far sooner and avoid a degradation in productivity.

The process would augment the value of the intelligence we aggregate and own and, as it’s a trusted source, accelerate decision making and the time to resolution (TTR). No human can realistically (nor would they want to) hold in their brain all the common problem scenarios and fixes.

I’ve tried to do this in my professional career, and though possible, it is exhausting. That’s why I believe, applying conversational AI to the common challenges our customers face would help highly qualified and skilled humans validate and implement the decisions they take.

I’d advocate that automating the interrogation of vast knowledge banks makes complete sense, especially when it helps skilled people get on with doing what they do best — running, managing, and developing world class ERP.

I should be clear that I am wedded to the notion that it’s important the wider industry runs the AI race in tandem with the ethics that protect humanity. We must thoroughly understand the implications at every point in development and put in place the checks, balances and regulation to ensure the values we hold dear are protected and enhanced, not obliterated.

In the world of AIOps there is real value to its adoption not least to ensure mission critical systems related to food supply or energy stay online. We must therefore consider the broad view of AI technology as well as our narrower domain. Only with a balanced view and appreciation of the accountability we assume as leaders, can we make the right choices.

John Appleby leads Avantra as the Chief Executive Officer. Before Avantra John served as the Global Head of DDM/HANA Center of Excellence at SAP and as the Global Head of SAP HANA solutions at Bluefin Solutions, subsequently acquired by Mindtree. John is a recognized thought leader in the SAP market and was part of SAP’s Mentors Group. John holds an MA in computer science from the University of Cambridge.

The post Does Conversational AI Have A Role to Play in AIOps? appeared first on My TechDecisions.

The company says these improvements have improved Bard’s accuracy to computation-based word and math problems by 30%.

According to Google, the company is introducing a new technique called “implicit code execution” to help Bard detect computational prompts and run code in the background. The intended result is a more accurate response to mathematical tasks, coding questions and string manipulation prompts. These improvements also come with a new features that allows users to export a table to Google Sheets.

In a blog, Google leaders overseeing Bard say the improvements will make the generative AI chatbot better at answering questions such as:

• What are the prime factors of 15683615?
• Calculate the growth rate of my savings
• Reverse the word “Lollipop” for me

In the blog, Google says large language models (LLMs) are like prediction engines. Essentially, LLMs generate a response to prompts by predicting what words are likely to come next.

“As a result, they’ve been extremely capable on language and creative tasks, but weaker in areas like reasoning and math,” write Google Bard leaders. “In order to help solve more complex problems with advanced reasoning and logic capabilities, relying solely on LLM output isn’t enough.”

This new method, however, allows Bard to generate and execute code to boost its reasoning and math abilities.

According to Google, this approach is inspired from “a well-studied dichotomy in human intelligence, notably covered in Daniel Kahneman’s book “Thinking, Fast and Slow” — the separation of “System 1” and “System 2” thinking.

“System 1 thinking is fast, intuitive and effortless,” the Bard experts write. “When a jazz musician improvises on the spot or a touch-typer thinks about a word and watches it appear on the screen, they’re using System 1 thinking. System 2 thinking, by contrast, is slow, deliberate and effortful. When you’re carrying out long division or learning how to play an instrument, you’re using System 2.”

LLMs have been essentially operating under System 1, producing responses quickly but without deep thought, leading to some issues like trying to solve complex math problems.

Meanwhile, traditional computation more closely aligns with System 2 thinking as it is formulaic and flexible, but can produce impressive results with the “right sequence of steps,” Google says.

With the latest update, Google is combining the capabilities of both LLMs and traditional code – which it compared to combining System 1 and System 2 thinking.

“Through implicit code execution, Bard identifies prompts that might benefit from logical code, writes it “under the hood,” executes it and uses the result to generate a more accurate response,” Google says. “So far, we’ve seen this method improve the accuracy of Bard’s responses to computation-based word and math problems in our internal challenge datasets by approximately 30%.”

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Announced at Cisco Live, the features – powered by large language models – come as Cisco is also bringing generative AI to its security tools to help augment security analysts and simplify policy management and threat response.

The news also comes as competitor platforms such as Zoom and Microsoft Teams are announcing similar generative AI capabilities in their respective collaboration platforms also designed to help users become more efficient during and after meetings.

Cisco’s new Webex AI features, which will be available by the end of the year, include  “Catch Me Up”, which is designed to allow users to catch up on missed interactions such as meetings, calling, chats and more.

Meanwhile, intelligent meeting summaries will give users outlines of meetings with key points and action items. Users can opt in to automatically generate the most important parts of a Webex meeting, extract the key points and capture action items with owners.

Cisco’s vision is for intelligent meeting summaries to be a time-saver for attendees and employees with busy schedules who may be unable to make every important meeting. This also eliminates the need to listen to a recording or read through a transcript.

Similarly, Cisco Summaries in Vidcast, the company’s video messaging solution, that will produce highlights and chapters so viewers can navigate to the most important parts of the video quickly.

For contact center agents, Cisco is also bringing AI-powered conversation summaries to Webex Contact Center to give agents an automated way to consume long-form text from digital chats with customers as well as facilitating post-call wrap-up and resolution with customers, the company says.

Cisco gives the example of a customer call being escalated to a human agent where the customer expects the agent to be informed and not have to repeat what they’ve already told virtual agents and automated systems. Summaries give agents a clear summarization of issues and resolutions already explored via self-service and a summary of the call to both the agent and customer once it ends, the firm says.

In a statement, Jeetu Patel, the company’s executive vice president and general manager of security and collaboration, said generative AI will quickly become part of any business’ tools.

“At Cisco, we’ve been using AI for years,” Patel says. “Now we’re unveiling how we’re incorporating generative AI into our existing products, helping customers drive real value to unlock the most secure, unrivaled work experiences possible.”

The post Cisco Live: New AI-Powered Webex Meeting Summary Tools appeared first on My TechDecisions.

Specifically, the Zoom IQ tools launched today are Zoom Meeting summary and Zoom Team Chat compose, which leverage both large language models from ChatGPT creators OpenAI as well as Zoom’s own large language model.

Meeting summary allows Zoom Meeting hosts to create a summary powered by the company’s own AI models, and the hosts can then share it via Zoom Team Chat and email without having to record the conversation, Zoom says.

Hosts will receive automated summaries, which can be shared with both attendees and those who didn’t attend so they can catch up on what they missed.

Team Chat compose leverages OpenAI’s technology to enable Zoom Team Chat users to draft messages based on the context of a Team Chat thread in addition to changing message tone and length as well as rephrasing responses to customize text recommendations, the company says.

To use these Zoom IQ features, customers will need to go to the Zoom admin console and opt into the free trials for each feature. As part of the opt-in, customers will also select data-sharing options with Zoom. Account admins may change this data-sharing selection at any time. Customer data will not be used to train third-party models, the company says.

Zoom is working on several other AI-powered Zoom IQ features, including email compose, Zoom Team Chat thread summaries, meeting queries, whiteboard draft and whiteboard synthesize.

According to Zoom, its federated approach to AI leverages its own proprietary large language AI models, those from leading AI companies like OpenAI and Anthropic, and select customers’ own models.

Zoom wants that flexibility to incorporate multiple types of models to provide the most value for its customers’ diverse needs.

“With the introduction of these new capabilities in Zoom IQ, an incredible generative AI assistant, teams can further enhance their productivity for everyday tasks, freeing up more time for creative work and expanding collaboration,” said Smita Hashim, chief product officer at Zoom. “There is no one-size-fits-all approach to large language models, and with Zoom’s federated approach to AI, we are able to bring powerful capabilities to our customers and users through Zoom’s own models as well as our partners’ models.”

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According to Lucidworks, the combined solutions deliver a hyper-modern search experience where users get highly personalized, relevant, dynamic, real-time experiences that drive better key outcomes for businesses.

The joint partner solutions will offer accelerated relevance and user experience in:

• Use Case: Merchandising, customer service, B2B and B2C commerce, call center, conversational AI /chat, and enterprise workplace applications
• Vertical-based solutions: Financial services, travel and hospitality, telecommunications and media

“Advancements in large language models and conversational AI have created a new set of opportunities to optimize the customer experience,” said Carrie Tharp, VP of strategic industries at Google Cloud, in a statement. “As a longtime partner, Lucidworks is well-positioned to provide the guardrails, security measures and support enterprises need to get lasting value from conversational AI tools.”

With Lucidworks, Google Cloud customers gain additional ways to utilize Google Cloud spend through an expanded array of joint solutions available on the Google Cloud Marketplace, including Lucidworks Fusion. Companies can expect a fast time-to-value, better in-the-moment personalization, improved average order value (AOV), better margin and sell-through, and improved gross margin return on investment inventory (GMROI), says the company.

“It takes expertise, and modern technology, and integrated AI to deliver an exceptional experience—what we call ‘hyper-modern’,” said Mike Sinoway, CEO, Lucidworks, in a statement. “Today’s buyers and employees are smart, selective, and expect a lot. Brands need to make search their superpower. Our ongoing partnership with Google Cloud means we can deliver complementary search and browse solutions for every part of the customer journey.”

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According to the Austin, Texas-based cybersecurity giant, Charlotte AI lets customers ask natural language questions and receive answers from the Falcon platform, enabling anyone from the IT helpdesk to CIOs and CISOs ask questions to help secure their organizations.

CrowdStrike’s cybersecurity generative AI assistant

CrowdStrike says Charlotte AI initially addresses three main use cases: democratizing cybersecurity and giving every user the same capabilities, elevating IT and security productivity with AI-powered threat hunting, and automating repetitive tasks like data collection, extraction and detection.

Users can ask Charlotte AI questions such as “What is our risk level against the latest Microsoft vulnerability?” to directly gain actionable insights to inform decision-making and accelerating time to response.

Like other generative AI applications, Charlotte AI will also help level the playing field and give less experienced IT and security professionals the ability to make better decisions faster, essentially narrowing the cybersecurity skills gap and reducing response time.

According to CrowdStrike, Charlotte AI will leverage CrowdStrike’s data, including the trillions of security events captured in the CrowdStrike Threat Graph, asset telemetry from across users, devices, cloud workloads and the company’s threat intelligence research.

In addition, Charlotte AI will benefit from “a continuous, human feedback loop” from Across CrowdStrike Falcon OverWatch managed threat hunting, CrowdStrike Falcon® Complete managed detection and response, CrowdStrike Services, and CrowdStrike Intelligence.

In a statement, CrowdStrike President Mike Sentonas said CrowdStrike has pioneered the use of AI in cybersecurity to identify malicious behavior and combat advanced attacks. Charlotte AI is the next innovation that will help users of all skill levels improve their ability to stop cyberattacks and reduce complexity, he adds.

“Our approach has always been rooted in the belief that the combination of AI and human intelligence together will transform cybersecurity,” Sentonas said. “We believe our continuous feedback loop on human-validated content is critical, and because of this, no other vendor will be able to match the security and business outcomes of CrowdStrike’s approach to generative AI.”

Charlotte AI in action

In a blog post, CrowdStrike lists several examples of questions users can ask, including:

• “Do we have vulnerabilities involving Microsoft Outlook?”
• “What are the biggest risks facing our business critical assets?”
• “Are we protected against the Log4j vulnerability? Where are we at risk?”
• “Which threat actors target us?”
• “What are the critical vulnerabilities being exploited by these adversaries?”
• “Can you sweep my endpoint estate for any IOCs you found?”
• “What are the top recommended remediation actions for the impacted endpoints?”

Other questions can prompt Charlotte AI to find malicious activity, such as lateral movement involving Windows hosts, the company says.

CrowdStrike, AWS AI partnership

The private preview of Charlotte AI came a day before CrowdStrike and AWS announced that the companies are working on new generative AI applications to help companies accelerate their cloud, security and AI journeys.

CrowdStrike will be leveraging new generative AI applications of Amazon Bedrock, a fully managed service that makes foundational models from leading AI startups and Amazon available via an API, to help customers adopt advanced Falcon Platform search, reporting and automation, the companies say.

In fact, Amazon Bedrock was used to accelerate development of Charlotte AI, according to CrowdStrike.

In addition, the companies are also working on solutions to help keep customers safe across a range of AI and ML services as generative AI rapidly transforms the tech industry.

According to CrowdStrike, the company is extending the protection of CrowdStrike Falcon Cloud Security to AWS AI/ML services by providing native integrations designed to further prevent, identify and remediate security risks associated with the adoption of AI/ML.

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The company says this beta release of Photoshop is its first Creative Cloud application to integrate Firefly with an exciting roadmap ahead that will transform workflows across Creative Cloud, Document Cloud, Experience Cloud and Adobe Express.

Adobe has a history of AI features, such as Neural Filters in Photoshop, Content Aware Fill in After Effects, Customer AI in Adobe Experience Platform and Liquid Mode in Acrobat — each empower Adobe customers to create, edit, measure, optimize and review billions of pieces of content.

Firefly was launched in April 2023 with an initial focus on the generation of images and text effects. The company notes it has quickly became one of the most successful beta launches in company’s history, with beta users generating over 100 million assets.

Since its launch, Adobe has been expanding Firefly to support vector recoloring and generative fill. According to Adobe, Firefly is the only AI service that generates commercially viable, professional quality content and is designed to be embedded directly into creators’ workflows.

The company notes Firefly’s first model is trained on Adobe Stock images, openly licensed content and other public domain content without copyright restrictions. Enterprises will be able to extend Firefly with their own creative collateral in order to generate content that includes the company’s images, vectors and brand language. The integration of Firefly across Adobe Experience Cloud applications will allow marketing organizations to use Firefly to accelerate their content supply chain production.

Supercharging Creativity and Design

The company says the integration of AI across Photoshop’s core tools enables new creative workflows that supercharge ideation with precise creative control.

Generative Fill automatically matches perspective, lighting and style of images. Users can generate digital content in seconds with the following AI features:

• Firefly: Firefly is designed to generate images that are safe for commercial use and is trained on Adobe Stock’s hundreds of millions of professional-grade, licensed, high-resolution images, helping ensure Firefly won’t generate content based on other peoples’ or brands’ intellectual property (IP).
• Magically leap from idea to image, with simple text prompts: Add, extend or remove content from images to achieve astounding results.
• Edit non-destructively: Create newly generated content in generative layers, enabling you to rapidly iterate through a myriad of creative possibilities and reverse the effects when you want, without impacting your original image.
• Create at a transformative rate: Experiment with off-the-wall ideas, ideate different concepts and produce boundless variations of high-quality content as fast as you can type.
• Available as a web tool: Generative Fill is also available as a new module in the Firefly beta for users interested in testing the new capabilities on the web.

Content Credentials

As a trusted partner to individuals and businesses of all sizes, Adobe says, it develops and deploys all AI capabilities with a customer-centric approach and according to its AI Ethics principles to ensure content and data transparency.

Generative Fill supports Content Credentials, serving an essential role in ensuring people know whether a piece of content was created by a human, AI-generated or AI-edited.

Content Credentials are like “nutrition labels” for digital content and remain associated with content wherever it is used, published or stored, enabling proper attribution and helping consumers make informed decisions about digital content.

The technology was developed by the Content Authenticity Initiative, which Adobe founded and recently surpassed 1,000 members.

When will Generative Fill be Availabile? 

Photoshop’s Generative Fill feature is available in the desktop beta app today and will be generally available in the second half of 2023. Generative Fill is also available as a module within the Firefly beta app.

The post Adobe Brings Firefly-Powered Generative AI into Design Workflows appeared first on My TechDecisions.

Like other tech giants, the company used its event to make important announcements in generative AI to help accelerate its enterprise adoption with Red Hat OpenShift AI, as well as several new capabilities in its automation platform Ansible.

The company is also focusing on simplifying management for Red Hat Enterprise Linux and securing the software supply chain.

Let’s look at the more notable announcements from the Raleigh, North Carolina-based tech firm’s announcements at its Red Hat Summit event in Boston, Mass.:

Red Hat OpenShift AI and Generative AI

According to Red Hat, the company is building and expanding upon the capabilities of OpenShift and OpenShift Data Science with OpenShift AI to give IT operations leaders, data scientists and developers a unified solution to train, serve, monitor and manage the lifecycle of artificial Intelligence (AI) and machine learning (ML) models and applications.

The company says Red Hat OpenShift AI underpins the generative AI services of IBM watsonx.ai, IBM’s new AI platform designed to scale intelligent applications and services across all aspects of the enterprise.

Red Hat says OpenShift AI solves a few key issues: the infrastructure-intensive training of AI models and requirement of specialized platforms and tools before serving, tuning and managing the model. OpenShift AI provides the infrastructure consistency across training, deployment and difference, the company says.

OpenShift AI provides a standardized foundation for creating production AI/ML models, as well as running the resulting applications, along with the ease-of-use and cloud-to-edge deployment options of OpenShift, the company says.

OpenShift AI provides several technology partner offerings, including Anaconda, IBM Watson Studio, Intel OpenVINO and AI Analytics Toolkit, NVIDIA AI Enterprise and Starburst, as well as 30 additional certified partners as part of the OpenShift ecosystem.

Customers with regulatory and compliance requirements, including air-gapped and disconnected environments can use OpenShift AI on -premises, while customers can also develop models in the public cloud and deploy them on-premises or at the edge, the company says. This provides a unique hybrid MLOps environment that enables collaboration between IT, data science and application developers, the company says.

According to Red Hat, new enhancements to OpenShift AI include deployment pipelines for AI/ML experiment tracking and automated ML workflows, model serving with GPU support for inference and custom model serving runtimes, and model monitoring to help organizations manage performance.

Ansible Lightspeed with IBM Watson Code Assistant

OpenShift AI, Red Hat says, is the base of IBM’s new AI enhancements including IBM Watson Code Assistant, to deliver domain-specific AI to IT organizations and developer teams.

This is done by bringing IBM Watson Code Assistant to Ansible, giving users the ability to write Ansible Playbooks with AI-generated recommendations. This new service is designed to help drive consistent and accurate automation adoption across an organization, the company says.

According to Red Hat, Ansible Lightspeed is the next phase of its Project Wisdom initiative, making it available to users, contributors, customers and Red Hat’s partner ecosystem. The service integrates with Watson Code Assistant, which will be available later this year. This allows access to IBM foundational models to quickly build automation code.

Event-Driven Ansible

Sticking with the IT automation theme, Red Hat also announced Event-Driven Ansible, a scalable solution designed to expand how organizations activate automation as a reliable strategy across the hybrid cloud.

The solution, slated for availability in June, is for Red Hat Ansible Automation Platform 2.4 customers; is designed to connect infrastructure and application observability tools with enterprise-grade Ansible automation, helping IT teams to pre-determine and define rules to initiate automated responses to situations like unresponsive system processes or unauthorized access requests.

When an event is trigged, the solution automatically executives the desired action via Ansible Playbooks or direct execution modules, with the ability to chain multiple events together into more complex automation actions, Red Hat says.

Event-Driven Ansible integrates with event sources form third-party monitoring, observability and IT tools, including Cisco ThousandEyes, CyberArk, Dynatrace, F5, IBM Instana, IBM Turbonomic, Palo Alto Networks, with additional partner integrations to follow.

Supplementary Red Hat-developed content is available for Red Hat OpenShift, Red Hat Insights, AWS, Microsoft Azure, Google Cloud Platform and ServiceNow, the company says.

Red Hat Enterprise Linux management

To help organizations better manage Red Hat Enterprise Linux, Red Hat is launching new capabilities in Red Hat Insights to give IT teams more insight and management tools to find and resolve IT issues much faster across the hybrid cloud. The tools are available through any browser via console.redhat.com and are designed to unify the management of Red Hat Enterprise Linux deployments in a single user interface, the company says.

According to Red Hat, these expanded capabilities build on the information provided by existing Red Hat Insights’ predictive analytics, which can detect potential bugs, misconfigurations or security vulnerabilities using Red Hat’s expertise in running Linux platforms in critical production environments.

The enhancements allow IT administrators to fix bugs without needing Red Hat Satellite Server and act on server groups simultaneously using patch templates, as well as build standardized operating system images that comply with organization-specific requirements.

Red Hat Advanced Cluster Security Cloud Service

According to the company, Red Hat Advanced Cluster Security Cloud Service is a new service that brings together Kubernetes-native security capabilities with a fully Red Hat-managed offering to help organizations take a security-forward approach to building, deploying and maintaining cloud-native applications regardless of the underlying Kubernetes platform.

The managed service supports both Red Hat OpenShift on private and public clouds and non-Red Hat Kubernetes services across major cloud providers, including Amazon EKS, Google GKE and Microsoft AKS, bringing security coverage to containerized applications regardless of where they are deployed.

Organizations can scale security capabilities across multiple clusters, whether on-prem or in the cloud while lowering operational costs by reducing the learning curve for implementing Kubernetes-native security without sacrificing necessary capabilities or enforcement, the company says.

Red Hat Trusted Software Supply Chain

Red Hat announced its Trusted Software Supply Chain Solution designed to protect against software supply chain vulnerabilities. The company says two new cloud services, Red Hat Trusted Application Pipeline and Red Hat Trusted Content, are joining in preview mode the existing Red Hat software and cloud services, including Quay and Advanced Cluster Security (ACS), to advance the successful adoption of DevSecOps practices, and embed security into the software development lifecycle.

Essentially, Red Hat Trusted Software Supply Chain allows customers to more efficiently code, build and monitor software using proven platforms, trusted content and real-time security scanning and remediation.

The solution allows customers to import git repositories and configure container-native continuous build, test, and deployment pipelines via a cloud service; inspect source code and transitive dependencies auto-generate Software Bills of Materials and verify and promote container images via a release criteria policy.

Visit the Red Hat Summit newsroom to learn more about these announcements and others.

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