LLM in a Flash

Published on December 12, the new study titled “LLM in a Flash: Efficient Large Language Model Inference with Limited Memory” can revolutionize the iPhone experience. It could bring a more immersive visual experience and make complex AI systems accessible on iOS devices. A recent research paper by Apple reveals a groundbreaking method that can assist in implementing AI on iPhones.

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3D animated avatars from single-camera footage

Apple researchers present HUGS (Human Gaussian Splats) as a method to create 3D animated avatars from single-camera footage in the first study. In a statement made by principal author Muhammed Kocabas, the researchers claimed that their system could automatically separate a static scene from an animated human avatar in as little as 30 minutes using only a monocular video with a modest number of frames (50-100).

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Future demands of AI-infused services

Apple is looking ahead to the future demands of AI-infused services as it considers incorporating these breakthroughs into its product selection, which might improve its gadgets even further. If Apple’s new memory-allocation feature works as advertised, it might pave the way for a whole new category of apps and services to take advantage of LLMs in ways that weren’t possible before.

In addition, Apple is contributing to the larger AI community by publicizing its research, which could encourage other improvements in the field. That Apple is willing to do this shows how seriously it takes its role as a technological leader and its dedication to expanding human potential.

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Flash storage optimization

Using flash storage optimization, this method streamlines large LLMs. Another major development will occur when Apple incorporates sophisticated AI inside the iPhone. Two new research papers showcased this month by the Cupertino-based tech behemoth declared substantial advancements in AI. The study uncovered novel methods for efficient inference of language models and 3D avatars. This research delves into the difficulty of keeping model parameters in flash memory, running them into DRAM on demand, and executing LLMs that use more DRAM than is available. Data transfers from flash memory can be optimized with the use of the Inference Cost Model, which takes flash and DRAM characteristics into account.

To back up their claim, the researchers have utilized models like Falcon 7B and OPT 6.7B. According to the research, compared to conventional approaches, the models increased CPU speed by 4-5 times and GPU speed by 20-25 times.

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Why the users should be happy?

Users of Apple products, such as the iPhone, may profit substantially from the results of the study on efficient LLM inference with limited memory. Users will get access to greater AI capabilities with strong LLMs running efficiently on devices with limited DRAM, like as iPhones and iPads. Better language processing, smarter voice assistants, better privacy, maybe less internet bandwidth utilization, and, most significantly, making advanced AI available and responsive to every iPhone user—these are all features that come with the iPhone.

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Language Processing is the Most Popular

Within the artificial intelligence (AI) world, Large Language Models (LLMs) dominate the imagination of digital creators everywhere. ChatGPT and Drift, both conversational AI bots that produce human-like text, are neck in neck in popularity – with Drift inching past ChatGPT by 1%. We observed heavy AI/ML traffic from the United States and India. Typically, users leverage these AI chatbots as exploratory tools to help create content and integrate AI capabilities into other applications.

Australians Rate AI Applications Based on Trust, Friendliness, and Diversity

ChatGPT Making an Impact in Manufacturing…and the US

The manufacturing sector is generating massive amounts of ChatGPT transactions. In fact, manufacturing accounts for ~21% of transactions, with finance coming in second at 14%. The rapid adoption and heavy use of generative AI is potentially part of the Industry 4.0 trend – where the manufacturing sector is becoming increasingly digitized, connected, and modern.

Artificial Intelligence and the Trust Deficit: A Call for Greater Transparency

Since the United States is one of the most prolific generators of AI-related transactions across all verticals – including manufacturing, the widespread adoption of AI will have a big impact on productivity and efficiency, but it comes with additional risks that threat actors will try to exploit.

Securing Transactions Using Generative AI and Cybersecurity

After manufacturing, tech and finance make up ~18% and ~15%, respectively, of all AI/ML traffic. With the technology and finance sector being such heavy users of AI applications, it’s no surprise that these sectors are also blocking the most AI/ML-related traffic. The most blocked AI application is a popular AI chatbot.

The majority of these policy-based blocks are instituted to ensure that organizations do not suffer accidental data leaks, so 10% of all AI/ML-related transactions are immediately blocked using URL filtering policies – before a user has the chance to share potentially confidential information with the application.

Recommendations for Organizations on Generative AI and Cybersecurity

It’s inevitable that AI-powered tools that help employees produce good results at a faster rate will gain a strong foothold in the corporate world. Instead of fighting this trend, organizations should embrace, customize, and secure their employees’ daily use of AI-powered tools.

Get ahead of the curb by creating guidelines on how your employees should interact with applications like ChatGPT or Drift AI chatbot.

For example, emphasize to employees the importance of not entering material or confidential information into conversational AI bots and at the same time implement security controls to prevent confidential data from leaking out. Moreover, encourages employees to review thoroughly and fact-check content generated by AI tools. Represent AI applications for what they are – tools in a digital creators’ toolkit. Search engines are tools.

Spell checkers are tools.

Online translators are tools. These tools are all helpful but cannot completely replace a human being making both informed and intuitive decisions (not in its current form).

If your organization plans to create its own internal AI-powered application for employees, ensure that any AI project: follows the same secure product lifecycle framework as your other external and internal products, meets legal and ethical standards, and proactively adapts its usage and security policies to the rapidly evolving nature of AI technology.

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[To share your insights with us, please write to sghosh@martechseries.com]

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HARMAN HealthGPT enables healthcare enterprises to solve targeted problems by deep domain understanding, adaptability and continuous learning ability.

HARMAN, a wholly-owned subsidiary of Samsung Electronics focused on connected technologies for automotive, consumer, and enterprise markets, announced that its Digital Transformation Solutions business unit has launched the groundbreaking Healthcare Private Language Model (LLM), known as HealthGPT. HealthGPT represents a leap in healthcare solutions by leveraging generative AI to empower healthcare professionals, researchers, and institutions through advanced patient care, medical research, and decision-making.

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HealthGPT brings forward all the benefits of a LLM like natural language interaction and learnability, with a strong knowledge base established from a variety of healthcare data sets. The model, built using the key principles of responsible AI, has additionally been tested for accuracy and hallucinations using HARMAN’s own automated LLM testing framework and the results were further validated by the healthcare subject matter experts.

HARMAN HealthGPT is a private LLM, providing enterprises with more control over roadmap, privacy, compliance and security issues at optimized cost.

Key advantages of working with HARMAN HealthGPT include:

• Enhanced Clinical Insights: Provides real-time, context-aware clinical insights, aiding in decision making.
• End-to-end LLM Fine tuning framework: A comprehensive framework for fine-tuning Language Model (LLM) to achieve optimal performance.
• Data generation framework: A framework for creating high quality customized datasets to further fine-tune Language Model (LLM) for enhanced performance.
• Automated LLM evaluation framework: Completely automated solution to validate fine-tuned LLMs for quality of outputs, factual correctness, hallucinations, and toxicity.
• Cost Optimization: Advanced deployment techniques using quantization to significantly reduce model size and thereby processing costs by up to one-tenth.
• Drug Discovery and Research: Accelerate drug discovery and development by extracting valuable insights from clinical trial data.

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Nick Parrotta, President – Digital Transformation Solutions & Chief Digital and Information officer at HARMAN said, “The business value of generative AI cannot be overstated. Organizations that scale and implement swiftly will see significant competitive advantages, productivity gains and more – but only if they can unlock their data and move from general purpose applications to more specialized, domain-specific applications. At HARMAN, it is our mission to help our clients navigate this hurdle and create a competitive advantage long-term. HARMAN HealthGPT is an example of our capabilities that will help organizations utilize specific industry trained models to better solve unique problems and add value for customers. With our long-standing AI expertise and ability to develop effective LLMs, we’re equipped to help our clients move past challenging roadblocks and fully capitalize on the exciting promise of generative AI.”

HARMAN is seeing strong early results of the LLM training, and its AI and ML teams are well positioned with an end-to-end, tested framework to fine-tune more private LLMs, beyond the healthcare industry, to solve similar customer problems.

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[To share your insights with us, please write to sghosh@martechseries.com]

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Robust Intelligence, the leading end-to-end AI risk management company announced a partnership with MongoDB to help customers thoroughly and efficiently secure generative AI models enhanced with enterprise data. The offering combines Robust Intelligence’s real-time AI Firewall with MongoDB Atlas Vector Search for an enterprise-ready solution that enables responsible innovation.

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Recent advancements in generative AI have motivated companies to experiment with potential applications, but a lack of security, ethical, and operational controls may have exposed companies to unmanaged risk. This challenge is exacerbated when sensitive company information is used to enrich pre-trained models, such as connecting vector databases, in order to increase the relevance to the end user.

The technology partnership between Robust Intelligence and MongoDB helps solve these challenges. Customers can confidently connect MongoDB Atlas Vector Search to any commercial or open-source large language models (LLMs) for retrieval-augmented generation knowing that Robust Intelligence’s AI Firewall is validating the inputs and outputs. This provides real-time protection against prompt injection, PII extraction, hallucination, and many other risks.

“Generative AI introduces an unmanaged security risk, which is compounded when enriching LLMs with supplemental data,” said Lena Smart, Chief Information Security Officer of MongoDB. “Robust Intelligence’s AI Firewall solves this critical problem, giving enterprises the confidence to use LLMs at scale. Our partnership makes it easier for customers to use generative AI while also keeping their data secure with guardrails in place.”

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Through the integration, customers will also be able to pass AI Firewall logs to MongoDB to store historical data. This can be used to identify advanced security attacks that often manifest across a cluster of data points as opposed to a single data point, such as data poisoning and model extraction.

“Enterprises rely on MongoDB to streamline the development of AI-enriched applications. It’s essential that this sensitive information remains secure,” explained Yaron Singer, Chief Executive Officer and co-founder of Robust Intelligence. “We’re thrilled to bring the joint value of AI Firewall and MongoDB Atlas Vector Search to our customers.”

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 [To share your insights with us, please write to sghosh@martechseries.com] 

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GenAI provides hope for an equitable healthcare revolution, but advances in technology must never come at the cost of patient rights

The next generation of ethical Generative Artificial Intelligence (GenAI) provides new hope for an equitable healthcare revolution – but advances in technology must never come at the cost of patient rights.

This was the consensus amongst top African and American health AI experts who participated in a webinar about the impact of GenAI on public healthcare. The webinar was hosted by Vantage Health Technology – part of BroadReach Group, a social enterprise focused on health equity globally. Through Vantage, the company has provided AI-led health-tech support to multiple public healthcare systems and diseases including in Africa and the USA for close to a decade.

“The fundamental issue in healthcare, whether you are in Sub-Saharan Africa, Western Europe, or the USA, is that demand outstrips supply in terms of health services, doctors, nurses, and medications. In Sub-Saharan Africa, for instance, there are 0.2 doctors per 1000 people,” explains Dr John Sargent, co-founder of the BroadReach Group.

He says we are trying to deliver on an antiquated model of “sick care”, where there is a certain ratio of doctors to patients. “We need to change this paradigm to be more effective by matching the supply and demand sides of our health systems in new digital ways.” Dr Sargent, who is a Harvard alumni and former World Economic Forum Social Entrepreneur of the Year, says that while GenAI has the potential to revolutionise how healthcare supply and demand are balanced, it is not the be-all-and-end-all of health tech. “The aim is not to get distracted by a shiny new toy – we need to put the patient first by protecting privacy and training our models against bias. We must always remember that technology is just a tool in service of patient care and supporting the healthcare workforce to improve health outcomes.”

Using GenAI to tackle specific diseases such as HIV and AIDS

Jaya Plmanabhan, chief scientist at innovation consultancy Newfire Global who trains health AI models for a living, says he is particularly excited about how large language models could be trained to revolutionize virtual expertise on diseases such as HIV and AIDS. “We call these ‘Role Specific Domain Models’ and they have the potential to be programmed to know everything about a particular disease, to better guide healthcare professionals on how to treat patients. This is a tremendously exciting prospect in the mission to end new HIV infections by 2030.”

These Private Language Models (PLMs) become oracles on a subject and are especially useful in helping solve hard problems in HIV management, such as loss to follow-up – a term for patients who drop off treatment. “Trying to find patients is critical to ensure that they don’t become resistant to drugs due to skipping doses. We can make our outreach much more engaging through conversational messages in their mother tongue and this can help us get people back into the clinic and back into care,” explains Ruan Viljoen, Chief Technology Officer of the BroadReach Group.

Start With the Problem, Not the Solution

“There is a quote that says we should fall in love with the problem, not the solution, which in this case is AI,” says Viljoen. “I believe the biggest challenge is still health inequity – healthcare access can vary depending on race, location, or age.”

Viljoen said GenAI can help solve practical problems, such as front-line healthcare workers being overburdened and not having enough time. “What are the repetitive, administrative tasks that are stealing their time? For instance, GenAI can help nurses with automated note-taking in patient interviews, relieving an administrative burden. The goal is not to replace the role but to free up their time for value-added work.”

One of the greatest uses of AI in health is to help healthcare workers focus on the next best action. “We can use large datasets and extract insights to help healthcare workers, delivered via easy-to-digest and secure messaging like emails or text messages. This is nothing new – we’ve done this in some form for nearly a decade using our AI-enabled platform, Vantage. What I’m most excited about, is how we can augment the quality of the interactions to bring together human and artificial intelligence.”

Heeding the risks and creating guardrails

Vedantha Singh, an AI ethics in healthcare researcher and virologist from the University of Cape Town, said the top ethical considerations for AI in healthcare are privacy, accuracy, and fairness. She urged at all AI systems should start with guardrails and ethics within their foundational design.

“There is a perception that there are no regulations for the use of AI in healthcare, but to assume we are operating in the wild west is not true. International bodies are sharing guidelines and regulation is slowly evolving – including in Africa. Egypt, Rwanda and Mauritius already have strong AI policies,” says Singh. This includes an emphasis on human labor not being completely replaced and giving patients agency over how their data is used.

Singh says that companies must embed ethical guardrails – aka ‘guardrails by design’ in their health products from the start. Plmanabhan adds that GenAI can reduce costs and personalize care, but it must be used carefully. “For example, if the data is biased, the model will be biased. GenAI can also be used to create fake patient profiles to commit fraud.” Unbiased, quality data which complies to regulations such as HIPAA and POPIA or GDPR must be prioritised.

Plmanabhan emphasises the importance of patients giving informed consent, knowing how GenAI is being used on their data. “We need to stay committed to immovable core principles – we cannot compromise on the human in the middle of it all.”

Reaching the hardest to reach patients

Viljoen says GenAI is not just improving healthcare for urban patients. Those in deeply rural areas could benefit too.

“Internet connectivity and satellite communication are becoming more ubiquitous. A few things provide hope: Big cloud providers are providing more ‘edge computing’ for rural areas, the mobile phone is becoming a very powerful computer in the pockets of people all around the world, and small rural clinics can use smaller GenAI models which require smaller amounts of data and computing power– they don’t need to use ChatGPT,” says Viljoen.

Plmanabhan explains that there are secondary GenAI models that can function offline. The primary models are always online, with and secondary models sending information back to the primary model once it is back online.

Hope for an equitable healthcare revolution

GenAI can increase affordable and equitable healthcare through the automation of routine tasks. To create a world where more equitable healthcare exists, it is critical to establish strong partnerships between donors, policy makers, researchers, and healthcare implementers.

Viljoen concludes, “We need to be experiment rapidly with AI, and deploy cautiously. It’s an incredible time to work in health technology and to see how we can use it to at last achieve health equity.”

SOURCE – BroadReach Group

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Semalytix’s PatientGPT is a patient-centric Large Language Model (LLM) approach for Life Sciences applications. The new generative AI tool showcases a smart interplay between supervised machine learning and LLMs that are trained on millions of real-time patient experience data sourced in 26 different languages. According to the company that made PatientGPT, the new generative AI tool is capable of replacing all the existing 6 months’ worth of research and documentation within a few seconds. Considered as a milestone in patient-centric healthcare and drug development industry, PatientGPT could surpass all the previously known capabilities of AI and machine learning in the life sciences industry.

AIThority readers would know that this is not the first PatientGPT in the modern healthcare industry. Earlier, PatientGenie had also launched its version of “PatientGPT”. PatientGenie’s AI platform is directed at patients and healthcare managers seeking better personalized responses from the existing healthcare systems while navigating through the different processes at various stages of diagnosis, treatment and follow-ups.

Generative AI tools built on Large Language Models (LLMs) are simplifying drug development process in the pharmaceutical industry. Despite rapid advancements in the pharma industry in the post-pandemic era, a majority of drug developers are still fighting it out for efficacy and timely delivery. By bringing in supervised ML and LLMs to life sciences, PatientGPT, a new kind of generative AI model, could accelerate the entire lifecycle in a safer environment for handling sensitive patient data. This new AI tool could complement Semalytix’s real-world evidence generation platform, called Pharos.

Semalytixs’ co-founder and Chief Product Officer, Janik Jaskolski said, “We can not only analyze but also precisely query the effects of any medication based on patient experiences and unmet needs worldwide, down to the smallest detail. By the end of the year, we will have over 50 million patient data points in our patient experience data archive, which our own LLM solution will be tuned with. This allows us to gain exact insights into how people live with diseases and generate crucial new knowledge for patient-focused drug development.”

Benefits of PatientGPT

Janik’s colleague and co-founder at Semalytix, Prof. Dr. Philipp Cimiano (Chief Technology Officer) added, “PatientGPT truly unlocks the huge potential of patient experience data that will help shape new therapies and authentically and continuously answer what patients need most.”

Janik said, “The most popular example from recent history is the accidental invention of the potent drug Viagra in 1998, which was originally a side effect of the tested hypertension medication and repurposed based on patient experiences. Thanks to modern technologies, this kind of learning can now be amplified. “With access to over 100 million sources in 26 different languages, and combining patient experiences from around the world over the past 10 years, the treasure of global patient knowledge already resides in our Pharos platform.”

A well-oiled and highly data-intensive supervised AI framework governs the working and outcomes of PatientGPT at Semalytix. This ensures PatientGPT outcomes are accurate and hallucination-free, which makes it easier for the analytics team at life sciences organizations to fact-check every bit of information in real time. The new AI tool would boost evidence-based drug development for chronic diseases such as cancer, diabetes, and mental illness. Founded in 2015, this AI pioneer is already fulfilling the needs of the top 20 pharma companies.

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The quantum leap in the area of generative AI has introduced massive reforms in the areas of marketing, customer service, sales, virtual designing, and online messaging. Generative AI — its future is still hard to predict. But, for most business leaders, it is already reshaping the industry use cases with creativity at the center of its existence and further growth. Salesforce, the leading CRM software maker, is charging its way forward with new capabilities for enterprise users. It has built a fully-secured generative AI cloud platform to bolster its current position in business intelligence and automation. Labeled as AI Cloud built for the CRM market, Salesforce’s latest offering sets a new benchmark in generative AI for enterprises. The new wave of generative AI tools could break down all the barriers to communication and collaboration between a brand and its customers. The good thing is– Salesforce AI Cloud could be a strong catalyst in eliminating the dark side of generative AI’s use.

The Human-Computer interface, powered by generative AI and large language models (LLMs) could drive a seven percent growth in the global GDP, which is almost $7 trillion, in the next 10 years. 

So, what exactly is Salesforce AI Cloud?

Salesforce AI Cloud is a CRM suite of generative AI features for a wide range of business applications and workflows. Salesforce’s AI platform, Einstein, is at the core of its newly launched AI Cloud, powering a trustworthy, real-time, open, and secured enterprise-ready generative AI for superior customer service and experiences. The purpose of Salesforce AI Cloud is to help every organization in expanding its productivity through a blend of new AI and automation capabilities across Sales, Marketing, Service, and Commerce.

Why Did Salesforce Bring in Generative AI to its CRM?

According to research, 43% of marketers don’t know how to tap generative AI for best results.

Salesforce, one of the world’s leading cloud CRM makers, leverages generative AI to ensure users are always prepared to pull in customer data in a secure and bias-free way. Salesforce AI Cloud, with the various GPTs and LLMs in sync, creates a viable ecosystem for generative AI applications. Marc Benioff, Chair and CEO of Salesforce highlighted the need to bring in generative AI inside the CRM. Marc said, “AI is reshaping our world and transforming business in ways we never imagined, and every company needs to become AI-first. AI Cloud, built on the #1 CRM, is the fastest and easiest way for our customers to unleash the incredible power of AI, with trust at the center driven by our new Einstein GPT Trust Layer. AI Cloud will unlock incredible innovation, productivity, and efficiency for every company.”

Here are the five most useful features of Salesforce AI Cloud that could disrupt the customer experiences and services industry.

Solid Gen AI Foundation for Cross-functional Teams

Salesforce AI Cloud supercharges different cross-functional operations by bringing together data analytics, automation, and AI-powered self-service. For instance, sales teams can use the generative AI features to create highly personalized emails based on customer’s needs and requirements. Marketers can deliver hyper-personalized content for customers across the entire buyer’s journey. Commerce teams can design a customer-centric conversational AI workflow using generative capabilities for chat, website, mobile, email, and social media. Likewise, developers can also use the Cloud AI features for auto-generating complex codes, fixing bugs, and troubleshooting issues in the project with better agility and higher accuracy.

So, the AI Cloud from Salesforce is best for multi-functional business groups that are focused on content personalization, marketing automation, and AI-led conversation management for better customer experience management.

Filling the Trust Gap

Business leaders are aware of the limitedness of security barriers involved with generative AI platforms. In some organizations, the use of generative AI tools such as ChatGPT is completely prohibited. The users are warned about the complex security issues that arise from using unknown features. These new features could become vulnerable surface attack points of data breach and malware injection.

Salesforce intends to overcome this problem with Einstein.

Generative AI development at Salesforce is infused with data security, privacy, and trust. In a survey related to the ethics of generative AI models, 73% of employees feared that embracing generative AI could introduce new types of risks in the system. 60% of users who plan to deploy generative AI within their enterprise are still clueless about the technology’s adherence to data privacy and security norms. Salesforce AI Cloud could solve these problems for enterprise users. The answer lies with the new Einstein GPT Trust Layer — an advanced data security framework that prevents LLMs from retaining and misusing sensitive customer data from the CRM or data lakes.

Shohreh Abedi, EVP, Chief Operations Technology Officer, and Member Experience at AAA – The Auto Club Group, said – “Our goal is to deliver more personalized member engagement, make our processes more efficient and cost-effective, and drive innovation across our team within a safe and trusted environment. We’re accelerating our digital transformation with Salesforce, and AI Cloud will help us implement AI across our entire business, including DevOps, support, sales, and underwriting.”

Integrating with Third-party LLMs

Implementing AI tools with superior features can help companies with higher levels of productivity. However, it could be a complex problem for users who have to choose between different LLMs and AI platforms to get their work done using automation and business analytics. Things could change for CRM users with Salesforce AI Cloud.

Salesforce CRM users can reap the benefits of using generative AI offerings available in the open ecosystem. The “right model for the right tasks” approach could save users time and cost, whilst maintaining a seamless integration with LLMs from OpenAI, AWS, Anthropic, Cohere, and others. All the prompts and responses are adjusted to meet the requirements of Salesforce customers. It is worth a mention here — OpenAI’s Enterprise API and the Einstein GPT Trust Layer would jointly deliver secured content moderation to keep the data retained inside the Salesforce Cloud infrastructure.

Interestingly, Salesforce will also open access to its Salesforce LLMs to fundamentally shift the pace of development and innovations for CRM users with faster code generation, analytics, and process automation. Currently, Salesforce’s family of LLMs includes CodeGen, CodeT5+, and CodeTF. These LLMs are already helping customers enhance their business agility and productivity with effective outcomes across multiple operations. Additionally, Salesforce has also developed generative AI tools such as Marketing GPT, Sales GPT, Service GPT,  Commerce GPT, Slack GPT, and Einstein GPT to empower employees and organizations with trusted customer data-based content generation and knowledge-sharing platforms that can make faster, accurate decisions across a wide range of corporate scenarios.

“Embedding AI into our CRM has delivered huge operational efficiencies for our advisors and clients,” said Greg Beltzer, Head of Tech for RBC US Wealth Management. “We believe that this technology has the potential to transform the way businesses interact with their customers, deliver personalized experiences, and drive customer loyalty. We are excited to explore this opportunity with Salesforce and drive the next generation of personalized customer experiences.”

Prompt Builders (Without Hallucinations)

The generative AI responses are only as good as the prompts that derive the outcome. It could take months to master the art of generating responses from your business-centric prompts. For complex industries and business operations, this could take longer. But, are they effective?

The initial days of the modern generative AI era have revealed a big problem for users. It’s related to the artificial hallucinations in the responses that look like realistically accurate text but are scientifically wrong information. The problem of hallucinations could be a major pain point in industries that rely on accurate scientific data such as in healthcare, biomedical research, scientific reporting, and medical education. Other industries could also suffer due to hallucinations induced by natural language generators (NLGs) if the prompt is not regulated by trained machine learning algorithms.

Salesforce AI Cloud is bringing a simplified approach to hallucinations-free AI prompts. These prompts are ideal for use in Marketing, Sales, Service, and Commerce teams, where users may have to deal with millions of data points to generate context-rich responses. These optimized AI prompts are created by keeping the company’s brand and contextual messaging targets in mind.

Business-ready AI-based Data Analytics 

Data analytics is the future of artificial intelligence. The faster the decision-making process using AI, the bigger would be the ROI from using CRMs enriched with AI capabilities. Salesforce provides deep learning, generative AI, and predictive intelligence — all these come together like a binding force for Salesforce users. The AI Cloud taps the enormous capabilities of the Data Cloud, MuleSoft automation, Einstein, Tableau Analytics, Slack, CRM, and Sales Cloud.

By bringing Salesforce AI Cloud to the center of business operations, organizations can overcome all the major challenges of using generative AI at an enterprise level. The recent advancements in generative AI innovations at Salesforce would have far-reaching implications for the SaaS industry and the customers it serves. Generative AI can transform so many areas of business in an organization with little training. Companies such as Salesforce empower customer-facing teams with powerful data analytics, AI, and automation that unify their first-party data with trusted contextual campaigns in a trustworthy and secured ecosystem.

The post Salesforce AI Cloud: A Generative AI CRM Platform for the Salesforce Economy appeared first on AiThority.

The use of Artificial Intelligence (AI) used to be the stuff of dreams for event planners, but with the introduction and growing popularity of tools such as ChatGPT, these dreams could soon become reality. Launched by OpenAI back in June 2020, the AI-based chatbot is creating waves of excitement. With the ability to provide incredibly detailed and human-like responses to highly demanding queries, ChatGPT has the potential to revolutionize the way we gain information.

However, in the event industry, there are still many which question whether the technology will ever be ready for more creative and critical-thinking tasks.

So could AI help plan your next event? And what does it mean for the role of the traditional event organizer?

AI Opportunities for Event Organizers

A recent study showed that three quarters (75%) of CEOs and business decision makers feel that artificial intelligence (AI) would improve the events industry.

Not only is there clear appetite for tools that can help produce more exciting and engaging conferences, but there are also several different ways organizers can utilize the technology for their next event:

Chatbots and Live Requests

AI helps automate a lot of the repetitive, admin-type processes that conference organizers have to go through. For example, rather than having a human constantly monitor live discussion boards, AI-based chatbots can take care of any participant questions and make accurate suggestions based on their query. This frees up staff time to do more personal and proactive work, such as setting up live polls or taking care of any technical difficulties.

Create Tailor-Made Experiences

Why try and guess what potential attendees will want from your event when you can use AI to accurately predict it?

For example, smart registration systems can look at factors such as location, demographic, age, and interests to help inform content curation and speaker sessions. 46 percent of organizations featured in EventMB’s Event App Bible offered AI-powered attendee matchmaking, and the deep-learning capabilities of AI means it can also accurately suggest which sessions they should attend, which people to network with, and advertise relevant upcoming events.

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Interpretation and Translation

AI-powered automated captions and speech translation technology can completely remove language barriers for events to increase audience accessibility and reach. By taking live speech and translating it into a language of the user’s choice in real-time, attendees can enjoy any event and topic of discussion. The perfect live captions provider uses state-of-the-art AI engines and optimizes them to match your event’s content needs.

Measure Engagement and Success

AI can gather a huge amount of measurable data that can be used to optimize future events and prove return on investment (ROI). For example, analyzing attendances during the event can show which sessions were more popular and created the most leads, which can then be used to drive future marketing campaigns. Through chatbots, registration forms, and feedback surveys, AI can also allow event planners to collect and analyze data from a diverse audience that can help identify new trends and tips to improve upon.

Extra resources

AI apps can also help provide additional resources, reduce the impact of staff illnesses, and save costs on part-time staff. We’re already seeing this with event check-in areas and digital signage boards where AI can pick up some of the admin-level responsibilities while staff provide more personalized support. For attendees joining online, the hyper personalisation of AI can make it feel like they have access to their own assistant that knows exactly what they’re looking to get from the event.

What Is the Future of AI for the Events Industry?

While ChatGPT has certainly helped raise the profile of the potential future use cases of AI, there are plenty of opportunities event organizers can jump on right now to deliver more personalized and engaging experiences.

The arrival of ChatGPT and continued popularity of AI could help drive the wider adoption of large language models (LLMs). The cornerstone of AI-based chatbots, LLMs, can form responses to queries as well as learn from historical data and information. So, rather than fielding basic customer support questions like some chatbots do now, one single LLM can provide various uses for an endless number of scenarios.

ChatGPT could inspire similar AI-based tech to become more widely adopted – for example, helping rapidly produce content for presentations, topic research, and even source speakers. Until that time however, by utilizing even just some of the AI-based tools that are available right now, event managers can make better-informed decisions today that can help optimize the events of tomorrow.

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