Is It Possible for AI to Advance Our Understanding of the Natural World?

In his welcoming remarks, National Academy of Medicine President Victor Dzau emphasized that AI is helping science in many ways, including by predicting outcomes from data, simulating complicated scenarios, and finding relevant trends in huge datasets.

According to a report in the Nature journal on December 21st, researchers from Carnegie Mellon University made history by creating the first ever non-organic intelligent system to conceive, plan, and carry out a chemistry experiment.

Read:The Top AiThority Articles Of 2023

Features

Assistant Professor of Chemistry and Chemical Engineering Gabe Gomes, along with Daniil Boiko and Robert MacKnight, who were doctoral students in chemical engineering, created the system known as Coscientist. The complete spectrum of the experimental procedure is executed with a simple, plain English prompt using large language models (LLMs), such as OpenAI’s GPT-4 and Anthropic’s Claude.

The research team proved in their Nature paper that Coscientist can do things like plan the chemical synthesis of known compounds, navigate hardware documentation, control liquid handling instruments, complete scientific tasks involving multiple hardware modules and diverse data sources, and solve optimization problems by analyzing previously collected data. They also used the documentation to execute high-level commands in an automated lab called a cloud lab.

Read: State Of AI In 2024 In The Top 5 Industries

Cloud Lab

In early 2024, the first academic cloud lab will be opened by Carnegie Mellon in collaboration with ECL. More than 200 pieces of equipment will be available to academics and collaborators at Carnegie Mellon University through the Cloud Lab. Gomes intends to keep working on the technologies detailed in the Nature article so that they can be utilized by the Carnegie Mellon Cloud Lab and other autonomous research facilities down the road.

Additionally, a coscientist essentially unlocks the “black box” of research. By tracking and documenting each stage of the research process, the system ensures that the work can be easily reproduced.

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Massive Artificial Intelligence Database LAION

Until recently, experts focusing on child abuse believed that unregulated AI tools could only create child abuse images by merging their knowledge of adult pornography and non-harmful child photos.

The massive artificial intelligence database LAION contains over 3,200 photos of children who may have been sexually abused. This database has been used to train top AI image-makers like Stable Diffusion. Together with the Canadian Centre for Child Protection and other anti-abuse organizations, the Stanford University watchdog group was able to detect the inappropriate content and notify the authorities of the original photo connections. It claimed that about 1,000 of the pictures it discovered had been independently verified.

Read: 4 Common Myths Related To Women In The Workplace

Malicious Content Creation

There was a lightning-fast reaction. While the Stanford Internet Observatory’s study was expected to be released, LAION informed The Associated Press that it would be temporarily withdrawing its datasets. In a statement, the nonprofit organization known as LAION (Large-scale Artificial Intelligence Open Network) stated that it “has a zero-tolerance policy for illegal content, and in an abundance of caution, we have taken down the LAION datasets to ensure they are safe before Republishing them.”Stability AI, a London-based firm that makes the Stable Diffusion text-to-image models, is a notable LAION user who contributed to the development of the dataset. While recent updates to Stable Diffusion have significantly reduced the likelihood of malicious content creation, a previous version from last year—which Stability AI denies releasing—is still embedded in various applications and tools and is reportedly “the most popular model for generating explicit imagery,” as stated in the Stanford report.

Even though it isn’t always obvious, the LAION database is a source for many text-to-image generators. The developers of DALL-E and ChatGPT, OpenAI, have said that they do not employ LAION and have adjusted their models to reject requests for sexually explicit material involving children.

Read: Top 10 News of AWS in 2023

The Stanford Internet Observatory Thoughts

The Stanford Internet Observatory has called for more extreme measures because it is difficult to clean up the data retroactively. “Delete them or work with intermediaries to clean the material” is one option for everyone who has constructed training sets using LAION-5B, which is named for the over 5 billion image-text pairs it contains. Another option is to completely remove an earlier version of Stable Diffusion from the internet, leaving only the most obscure places for it. For instance, Thiel criticized CivitAI, a platform popular among those who create AI-generated pornography, for what he perceived as a lack of safeguards that would prevent the creation of photos of minors. Hugging Face, an AI startup that provides model training data is also urged to improve its reporting and removal processes in the study.

In light of safeguards provided by the federal Children’s Online Privacy Protection Act, the Stanford paper raises the question of whether any images of children, no matter how innocent, should be inputted into AI systems without the permission of their families. To identify and remove child abuse content, tech corporations and child protection organizations already issue films and photographs a “hash”—unique digital signatures. If you believe Portnoff, you may apply the same idea to abused AI models.

Read:Top 10 News of Google in 2023

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Thaler’s Case Reached the Highest Court

In a landmark decision, the highest court in the United Kingdom rejected the idea of artificial intelligence programs being recognized as patent inventors, thereby putting robots on par with humans. The Supreme Court of Britain denied the patents that Stephen Thaler, founder of Imagination Engines Inc., had requested, which would have identified his artificial intelligence computer DABUS as the creator. Judgment on Thaler’s appeal was unanimously rejected by the judges, who ruled that “DABUS is not a person at all,” by the requirements of patent laws.

AI Machine DABUS

The developer from the US asserts his entitlement to the inventions made by the artificial intelligence machine DABUS, which he says built an autonomous food or drink container and a light beacon. Since DABUS was not a natural person, the IPO determined in December 2019 that the specialist could not formally name it as the inventor on patent applications. Both the high court and the court of appeals upheld the decision in July 2020 and July 2021, respectively. The five-judge bench of the Supreme Court unanimously rejected Thaler’s argument following a March hearing.

The courts were not required to decide on whether the AI actually generated its inventions; the DABUS issue centered on how applications are made under the Patents Act 1977 legislation. Patents are legal protections for innovative and useful innovations that meet specific criteria set down by the government. These criteria include being technically sound, novel, and capable of being manufactured or used. AI advancements, like OpenAI’s ChatGPT technology, have recently been under investigation for a variety of reasons, including the possible effects on education, the dissemination of disinformation, and the future of employment. In this context, Thaler’s case reached the highest court.

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Getting Started With Retrieval-Augmented Generation (RAG)

An artificial intelligence methodology for retrieval-augmented generation was created by NVIDIA to assist users in getting started. It comes with everything customers need to build apps using this new approach, including a prototype chatbot.

The process incorporates NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server, two platforms for executing generative AI models in production, together with NVIDIA NeMo, a framework for creating and tailoring such models. All of these programs are a part of NVIDIA AI Enterprise, a platform that helps companies build and launch AI systems faster while providing the stability, support, and security that these systems require.

Data movement and processing on a vast scale is necessary for optimal RAG workflow performance. With 8 petaflops of computational power and 288 GB of fast HBM3e memory, the NVIDIA GH200 Grace Hopper Superchip is perfect; it can achieve a 150x speedup compared to a CPU. Companies may construct a broad range of assistants to support workers and customers after they are accustomed to RAG. They can integrate off-the-shelf or bespoke LLMs with internal or external knowledge sources. There is no need for a data center to run RAG. Thanks to NVIDIA software, which makes a wide variety of apps accessible on laptops, LLMs are now available on Windows PCs.

 

What Is the Difference Between a RAG and a Chatbot?

A RAG-based chatbot employs a knowledge base that is built from crawled URLs to deliver contextually appropriate replies, unlike standard chatbots that may have trouble keeping information up-to-date or accessing domain-specific knowledge.

Key Take Aways From This Webinar

• Transformations created by the use of chatbots, AI assistants, and copilots

• How is RAG (retrieval-augmented generation) a powerful technique driving generative AI applications?

• Key use cases of AI-powered assistants

• How to build safe, secure AI chatbots that stay on task?

Speakers

• Amanda Saunders-Senior Product Marketing Manager, NVIDIA
• Jacob Liberman- Product Manager, NVIDIA

Read: 4 Common Myths Related To Women In The Workplace

NVIDIA’s AI Workflow

NVIDIA AI Workflows consist of a bundled product that includes the AI framework and the necessary tools for automating a cloud-native solution. AI workflows have pre-built components that are designed for business use and adhere to industry standards for reliability, security, performance, scalability, and interoperability. These components also provide flexibility for customization.

An ordinary procedure could be like the diagram provided below:

 

Each process in this stack includes opinionated direction and sample components at every tier. Additionally, information is provided on how to connect the AI solution with these components.

Hardware

The utilization of NVIDIA AI Enterprise necessitates the use of GPU-accelerated hardware or cloud instances that are compatible. Each process is accompanied by precise needs and specifications.

Infrastructure and Orchestration

The NVIDIA Cloud Native Stack serves as an illustrative Kubernetes distribution for deploying and orchestrating workloads.

Supporting Software

The NVIDIA Cloud Native Service Add-on Pack facilitates the deployment of a collection of components that are essential for performing various services commonly needed in a production enterprise setting, including authentication/authorization, monitoring, storage/database, and more.

Applications

The example microservices are presented as a sequence of Helm charts and tailored containers, which are deployed as part of the workflow. Their purpose is to showcase the process of customizing and constructing an AI application using NVIDIA frameworks, as well as integrating this application with other microservices and enterprise software components.

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

 

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Altova announced the release of Version 2024 of its desktop developer tools, server software, and regulatory solutions with important new features.

“We are excited to announce AI integration in multiple products to enhance developer productivity and creativity,” said Alexander Falk, President and CEO for Altova. “At the same time, we’ve added one of the most-often requested features to MapForce, which is support for PDF. The new MapForce PDF Extractor will be a game changer for unlocking the volumes of data previously trapped in PDF documents and accessing it for use in data integration and ETL processes.”

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New features across the product line include:

• AI Assistant in XMLSpy immediately boosts productivity for XML and JSON development tasks by generating schemas, instance documents, and sample data based on natural language prompts. The AI Assistant can also generate XSL, XPath, and XQuery code. Generated code can be copied, opened in a new document, or sent to the XPath/XQuery window for further review and refinement.

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• MapForce PDF Extractor is an easy-to-use, visual utility for defining the structure of a PDF document and extracting data from it. That data is then available for mapping to other formats in MapForce, including Excel, JSON, databases, XML, etc., for conversion, data integration, and ETL processes.
• AI integration in DatabaseSpy includes an AI Assistant for generating SQL statements, sample data, table relations, and more, as well as AI extensions to explain, pretty print, and complete SQL statements. This AI functionality can offer a significant productivity boost for users of the multi-database tool and SQL editor.

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• Split output preview for XML and database report design in StyleVision lets designers see the changes they make in a design reflected in the output in real time. The option splits the UI into two, side-by-side panes that show the design and corresponding output in HTML, PDF, Word, or text at the same time.

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

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Mumbai Ozemio introduces the world’s first generative AI assistant, ushering in a paradigm shift in learning and development (L&D) Advancing its innovative technology solutions, Ozemio emerges with a revelation that promises to reshape the fabric of talent transformation. 

Ozemio introduces MIO– an AI-powered breakthrough that reimagines how organizations learn, grow, and excel. It cuts the noise on the internet and helps user learn more efficiently through its focused approach to the subject at hand.

MIO harnesses a wealth of knowledge, customizing precise responses to meet users’ distinct learning needs. Moreover, it ensures users are no longer dependent on AI prompts by crafting tailor-made prompts through strategic keywords.

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MIO’s ability to self-learn supports a constant development of prompts, queries, and responses, improving understanding and providing quick knowledge support for learners. It adapts to provide precise and contextually relevant interactions through its dynamic learning strategy, demonstrating Ozemio’s unrelenting dedication to continuous improvement.

What truly sets MIO apart is its ability to craft unique experiences. No conversations are alike, as MIO instinctively aligns with user preferences, learning styles, and objectives. With MIO, every interaction becomes a tailored journey resonating with users.

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Two Unique Scenarios, One Visionary Solution: MIO operates at the union of human ingenuity and AI’s limitless capabilities. It has two distinct yet complementary modes of operation:

• Precision in Knowledge-Base Matching: MIO uses a curated knowledge base to offer precise responses, making knowledge accessible anytime, even offline.
• Enhancing Answers with AI: When the knowledge base isn’t enough, MIO employs advanced GPT technology to provide insightful answers by blending human-curated content with AI-generated responses.

With the introduction of MIO, Ozemio further established its position as an industry pioneer.

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Sharing his delight, Deeptanshu Tiwari, COO at MRCC Group, stated:

“The impact of Generative AI has ushered in a revolution in our approach to work, learning, and talent transformation. However, generative AI still has a long way to go before it can deliver a focused path of talent transformation. This is where MIO comes in — with a purpose to act as a learning guide and provide timely support to individuals grappling with questions during both formal and informal learning phases. MIO reflects our commitment to create a focused talent transformation journey by merging AI capabilities with human-centric goals.”

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

The post Ozemio Heralds a New Era in Talent Transformation With Innovative Generative AI Assistant appeared first on AiThority.

BlackBerry innovation acts as a Generative AI powered SOC Analyst to increase efficiency and reduce fatigue for CISO teams

BlackBerry Limited announced its new Generative AI powered assistant for Security Operations Center (SOC) teams. The enterprise-grade solution acts as a SOC Analyst providing Generative AI based cyberthreat analysis and support to enhance CISO operations. It leverages private large language models (LLMs) for greater accuracy and data privacy.

The solution, which will be available to BlackBerry’s Cylance® AI customers, predicts customer needs to proactively provide information rather than requiring users to manually ask questions and compresses research hours into seconds. Fully integrated in the Cylance Console, it produces a natural workflow instead of an inefficient chatbot experience. Cylance launched as the industry’s first AI cybersecurity solution and the industry’s first predictive cybersecurity solution. Predictive cybersecurity is a must for emerging cyber risks and seen as the future of cybersecurity.

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“BlackBerry pioneered the AI cybersecurity market and our commitment to innovation means we are once again at the forefront of the industry as we unveil our Generative AI powered cybersecurity assistant,” said Nathan Jenniges, SVP & GM Spark, Cybersecurity Business Unit, BlackBerry. “This new solution will enable our customers to modernize their SOC operations helping them to stay a step ahead of the adversary. It will be invaluable to CISOs in overcoming the challenges they face, including an evolving threat landscape and resource constraints.”

BlackBerry has delivered transformative innovation for almost forty years and continues to set a standard in the technology industry. In the field of AI this is evidenced by the company having more than five times the AI/ML patents than competitors and AI being integrated across the company’s product portfolio. Furthermore, BlackBerry earlier this month was one of the first signatories of Canada’s voluntary Code of Conduct on the responsible development and management of advanced Generative AI systems.

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“Generative AI has the potential to deliver tremendous economic value, making it an area of focus for BlackBerry. Our patent portfolio already includes Generative AI,” said Charles Eagan, Chief Technology Officer, BlackBerry. “Throughout our innovations we take our commitment to delivering enterprise-grade solutions seriously – innovations that deliver value rather than react to hype – as demonstrated by today’s announcement and our being one of the first signatories of Canada’s voluntary Generative AI Code of Conduct.”

BlackBerry’s Generative AI powered cybersecurity assistant will initially be made available to a select group of customers.

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

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Hi, Abhishek. Welcome to the AiThority Interview Series. Please tell us about your current role at LinkedIn.

I’m the VP of Product Management for LinkedIn Marketing Solutions, where I lead a team that builds products that help B2B marketers connect with buyers, reach new audiences, and unlock opportunities. As B2B marketers continue to face long purchase cycles, a shifting privacy landscape and continued economic uncertainty, our role in creating solutions to help them build their brands and reach decision makers in new ways has never been more important.

Generative AI for content and image creation is taking the industry by storm. What are the unique business scenarios where generative AI can be truly effective in marketing and sales? 

The number of touch-points and amount of data marketers can collect across the funnel are increasing. With only 5% of buyers in-market to make a purchase at any given time–and even fewer in an economic downturn–AI can help marketers identify and scale the right content for the right buyer at the right time along their buying journey, which can convert them to make a purchase faster. Generative AI can reduce the heavy work required for image creation and also help with customizing post-click experiences for better results.

Recommended: AiThority Interview with Florian Hillen, CEO and Founder at VideaHealth

What are the invisible challenges of getting started with generative AI tools? 

We surveyed nearly 2,000 B2B marketing leaders across the globe for our new 2023 B2B Marketing Benchmark, and found that aside from finding and acquiring customers, the biggest challenge facing CMOs is incorporating emerging technology, like AI, into their marketing strategies. The use of generative AI is rapidly growing–and we’re just beginning to understand the opportunities ahead of us. Whether you’re part of a small business or enterprise, we’re learning as we go. We’re putting the technology in the hands of our members and customers to help them reduce the time they’re spending on routine tasks, so that they can focus on the more strategic aspects of their job to be more productive and successful in their work.

My suggestion to marketers will be to not be afraid of trying things out and have a learning mindset.

Please tell us about LinkedIn’s AI Copy Suggestions. How does it help Marketing teams scale their content production goals? 

AI plays a foundational role in connecting marketers, sellers and buyers across our platform. While AI is taking the industry by storm now – it’s not new for us at LinkedIn. We’ve used it for years to help marketers reach the right audiences at the right time, measure conversions with accuracy, train our bidding models, and aggregate signals–like intent–to help reach buyers.

According to our B2B Marketing Benchmark, we know that 55% of marketers plan to use generative AI to increase their efficiency so they can focus on higher value work. We developed AI Copy Suggestions that use advanced OpenAI GPT models to leverage data from LinkedIn Pages and Campaign Manager settings, like objective, targeting criteria and audience, to suggest ad headlines and copy to help marketers jump-start their campaigns and create more content in less time. It’s no secret that marketers are stretched to do more with fewer resources while continuing to drive ROI for their companies. We created the tool to help them jumpstart their creativity and reduce the time they spend on day-to-day tasks so that they can continue to focus on building their brands.

What are the true performance benchmarks to measure the effectiveness and quality of AI-generated content for marketing? How do you measure these at LinkedIn? 

It’s getting harder for marketers to prove that their strategy is working amidst an incredibly complex and group-based purchase cycle. We understand these challenges and are working alongside our customers to build measurement solutions to help them continue to map their campaigns to business revenue.

We are closely monitoring metrics like click-through and engagement rates for customers that use AI-generated copy, which can help them make data-backed decisions based on how the content is performing. We also created the CMO Scorecard, a tool that attaches quantitative results to brand advertising, to help marketers understand the creative, media, and outcome metrics they should measure to show the effectiveness of their brand marketing, so that they can continue to drive campaigns that impact the bottom line.

What’s next in LinkedIn’s Gen AI roadmap? How do you plan to generate more revenue using AI and deep learning capabilities? 

We’re always thinking about ways to augment what buyers and sellers can accomplish with AI and are continuing to infuse the technology into our solutions to help them focus on higher value tasks. We’re excited about our path forward and believe that we’re uniquely positioned to help B2B professionals connect to create content, up-level their creativity, and be more efficient in developing campaigns that resonate.

This is the beginning of our journey with GAI—there’s so much opportunity ahead, and we’re looking forward to sharing more about our plans later this year.

Recommended: AiThority Interview with Brigette McInnis-Day, Chief People Officer at UiPath

Thank you, Abhishek! That was fun and we hope to see you back on aithority.com soon.

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

• About Abhishek
• About LinkedIn

About Abhishek

Abhishek is a Product Leader at LinkedIn. Previously, he has built products and businesses at Twitter, Amazon, Adobe, and Yahoo! He has also been an entrepreneur, founding and leading Unboxed, a startup.

He has led product and engineering efforts for both consumer and enterprise products and platforms, with expertise and experience in personalization, marketplaces, and monetization.

About LinkedIn

LinkedIn connects the world’s professionals to make them more productive and successful and transforms the way companies hire, learn, market, and sell. Our vision is to create economic opportunity for every member of the global workforce through the ongoing development of the world’s first Economic Graph. LinkedIn has over 930 million members and has offices around the globe.

The post AiThority Interview with Abhishek Shrivastava, VP of Product at LinkedIn appeared first on AiThority.

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The emerging trends related to AI in Australia reveal the nation’s persistence in commercializing AI applications for better growth and prosperity. Australia is one of the fastest-growing AI innovation centers in the world. The rise of generative AI has proved to be a watershed moment in the evolution of AI and machine learning research in Australia. Today, Australia’s AI software and academic market is worth nearly $5 billion. By 2030, AI-based digital innovations could add around $315 billion to Australia’s national GDP. By 2025, Australia could spend $3.6 billion on its AI systems, bringing cutting-edge technologies to manufacturing, banking and finance, education, healthcare, media, and mobility. However, the citizens see AI technology differently. The current generation embraces AI with “cautious optimism,” as per the latest research.

According to a survey of 1,000 adult Australian citizens commissioned by UserTesting, only 38% would ever trust AI with finances, 33% with driving, and 30% with health-related searches. The same survey found that 24% of Aussies agree about sharing their personal and financial data for savings. However, 74% of AI users also fear for their privacy while interacting with AI tools and platforms. Clearly, user sentiments vary while using and embracing AI more openly in everyday lives in Australia.

The research on AI in Australia is hugely dependent on collaborations with China, the US, the UK, India, and Iran. UserTesting’s survey points to Australian consumer attitudes and experiences evaluated based on their interactions, user-friendliness, trust and privacy, and time management. For instance, the report mentioned that an average Aussie thinks AI could save them nearly an hour (57 minutes per day) daily. For most Aussies, AI is a great time-saver, and therefore, we are witnessing a growing awareness of AI’s capabilities that improve the quality and depth of life.

From a business point of view, 68% of Australian companies have AI systems in place, and another 23% plan to do so in 2024. 1 in 5 Aussies already understands the different AI capabilities meant for use in customer service, search phrase generation, recommendation, video game development, content creation, and so much more. Despite its popularity, AI has its own detractors, who harbor reservations against its limitless potential. While 28% confide in AI’s use in business and domestic lives, 36% have reservations. Most Aussies view AI with caution and a vigilant stance toward important personal decisions in finance, driving, and medical cases.

Smartphone usage for AI applications is a booming landscape in Australia. 74% of respondents use AI on their phones, while others access AI or related technologies through their smart TVs and desktops. Most Aussies choose AI for simple tasks involving email management and shopping experiences. As time progresses, AI could become a mainstay in personal and professional lives in Australian households.

Trust, and User-friendliness

Aussies and AI have a very nuanced relationship at the moment. The survey has captured the trust quotient among Aussies with AI. This trustworthiness is related to AI’s usefulness and how well Aussies understand the science behind AI and machine learning. 20% of Aussies say they know AI very well, and they can differentiate between voice assistants, chatbots, and gaming tools. 41% of Aussies mention they have a moderate familiarity with AI applications. But, a substantial percentage of AI users are either unsure or unsatisfied with AI’s role.

That’s where the grey area in AI lies — Trust, especially in critical aspects.

Nefarious activities involving the use of AI in deep fakes, ransomware, phishing, and financial frauds have dented public trust. Safety policies regarding trustworthy and responsible AI governance require collaboration between technology innovation companies, the government, and the users. For shopping, AI-powered platforms are considered intrusive. Personalized programmatic ads running on AI models overwhelm shoppers. Most shoppers think AI is breaching their privacy for data misuse and identity theft. Aussies had similar reservations with AI-powered targeted content promotion for healthcare and personalized medical solutions shown to patients. All these could limit the potential of AI tools while companies fail to build trust and governance for their AI models.

Conclusion

For AI to succeed in the Australian landscape, companies should put their AI ethics policy into practice. It is done by integrating technology with human, environmental, and social values, applied with fairness and transparency. The latest survey shows the immense scope in improving AI applications for the general Aussie families and organizations.

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

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