Dive into the world of AI, tech, and innovation as we bring you the latest and most viral stories buzzing across the digital and AI arena, top thought leadership insights, and more.

From breakthroughs in AI and Machine learning to advancements across the tech domain, our curation keeps you informed and updated. Whether it’s a glimpse into the future of AI or a spotlight on ground-breaking research, these highlights will give you the overall tech industry updates.

Explore the frontiers of AI technology, one headline at a time.

1.Blackbaud Boosts AI and Reporting Via Microsoft Azure and Power BI

Blackbaud has integrated Microsoft Azure OpenAI Service and Power BI into its Impact Edge platform, enhancing performance, scalability, and reporting capabilities for corporate customers. These enhancements allow users to quickly generate detailed reports on social impact initiatives using data from employee engagement, philanthropy, and education programs. The integration also introduces Blackbaud Copilot, an AI chatbot that provides insights and creates content. Blackbaud is collaborating with Microsoft to align Impact Edge with security best practices and further enhance its AI capabilities. The platform will be generally available in the fourth quarter of 2024, with new features added continuously.

2.NVIDIA Announces Generative AI Models and NIM Microservices for OpenUSD Language, Geometry, Physics and Materials

NVIDIA has announced major updates to its Universal Scene Description (OpenUSD) framework, enhancing its application in robotics, industrial design, and engineering. This integration of NVIDIA Omniverse with Microsoft’s OpenAI Service and Power BI enables developers to create accurate virtual worlds and digital twins for AI development. New NVIDIA NIM microservices offer AI models to generate and understand OpenUSD data, accelerating applications in industries like manufacturing and automotive. Companies like Foxconn and WPP are already using these advancements for digital twins and AI content creation. These innovations promise significant growth in OpenUSD adoption across various sectors.

3.DecoverAI Raises $2 Million to Revolutionize Legal Technology with “Legal Brain” and Generative Defense

DecoverAI, a leading legal tech company, has raised $2 million in seed funding led by Leo Capital to revolutionize the legal industry with AI solutions. The company aims to create a “Legal Brain” AI system that enhances legal workflows, including eDiscovery, research, and case strategy. Their flagship technology, Generative Defense, offers rapid data access, transforming how legal professionals work. The funding will boost AI development, expand features, and enhance marketing. CEO Ravi Tandon emphasizes empowering attorneys with advanced tools. COO Kevin Van Horn calls it a game-changer, supported by Leo Capital’s focus on disruptive tech solutions.

4.Soluna Announces Sustainable AI Soluna Cloud Supported by Hewlett Packard Enterprise

Soluna Holdings, Inc. a developer of green data centers, is launching advanced AI sustainability cloud solutions in collaboration with Hewlett Packard Enterprise (HPE). This partnership aims to support AI training and customization for enterprises, using HPE’s direct liquid-cooled infrastructure and NVIDIA H100 SXM GPUs powered by nearly 100% renewable energy. Soluna Cloud will provide scalable, eco-friendly AI solutions, demonstrating AI expertise to HPE’s global customers. CEO John Belizaire and HPE VP Maryam Chaudry emphasize the partnership’s role in delivering high-performance, sustainable AI services. The initiative is backed by Soluna’s innovative Helix data centers and a 2GW pipeline of renewable energy.

5.Phrase Launches AI-Led Translation Platform on AWS Marketplace

Phrase, a leader in AI-based localization technology, has launched its cloud-based Phrase Localization Platform on AWS Marketplace. This platform allows businesses to easily access software solutions with flexible pricing and quick deployment options, streamlining procurement. The launch also includes participation in the ISV Accelerate program, which connects independent software vendors with AWS customers for faster customer acquisition. With advanced AI-powered automation and translation management, Phrase offers AWS customers fast access to localization tools, helping businesses enhance global content strategies and accelerate international growth without lengthy administrative hurdles.

6.Lenovo Accelerates ‘Smarter AI for All’ with Databricks Collaboration

Lenovo has partnered with Databricks to promote AI adoption among its customers. As a Databricks Consulting & Systems Integration (C&SI) Select Tier Partner, Lenovo will streamline data management and improve AI accessibility. Through its AI Center of Excellence (AI CoE), Lenovo will help businesses harness AI efficiently and at scale. This partnership will enhance generative AI use, data analytics, and innovation. Initially launching in the Greater China and Asia Pacific Region, Lenovo will offer comprehensive services globally. Lenovo’s CIO survey revealed that 90% view AI as transformative, yet many lack the skills to scale it. This collaboration aims to address these challenges and support AI integration.

7.Infosys and ServiceNow Expand AI Collaboration to Revolutionize Service Delivery

Infosys has expanded its strategic collaboration with ServiceNow to offer comprehensive managed services, leveraging the AI capabilities of the Now Platform and Infosys Enterprise Service Management Café (ESM Café) for business transformation. This collaboration aims to accelerate ServiceNow implementation for enterprises like First Abu Dhabi Bank (FAB) by 30-40%, providing faster time-to-value. The Infosys ESM Café, part of Infosys Cobalt, includes over 70 applications and offers AI-powered solutions to enhance service delivery and operational efficiency. This partnership highlights Infosys’s commitment to driving digital transformation and delivering exceptional business value through cutting-edge AI solutions for global clients.

Weekly Roundup: Expert Views on AI Trends

AiThority Interview with Questionnaire for Jean-Philippe Desbiolles – IBM Managing Director – Groupe Crédit Mutuel

AIThority’s Weekly Voice: From the Expert

Jean-Philippe’s role at Crédit Mutuel involves a strong partnership with IBM to drive technology transformation. Since 2016, Crédit Mutuel has invested in AI and quantum computing. Key achievements include using IBM Watson to enhance customer and advisor experiences and expanding with the IBM watsonx platform for generative AI. This partnership has freed up millions of work hours for advisors, improving client service. Ethical AI use, data privacy, and transparency are central to their approach. Crédit Mutuel also explores quantum computing for future improvements in financial services and security.

AiThority Interview with Jim Rowan, AI market activation Leader and Principal, Deloitte

Jim, a principal at Deloitte, leads the Applied AI practice. He helps clients transform their businesses using data and AI for better decisions. His work spans life sciences, healthcare, and telecommunications, focusing on large-scale AI and data projects. Deloitte aids businesses by modernizing data strategies, improving infrastructure, and aligning data with company goals. Jim highlights the importance of AI in enhancing efficiency and customer experiences through chatbots and virtual assistants. Challenges include balancing deterministic and probabilistic AI models and addressing compliance and governance issues. Deloitte also explores innovative AI applications in various industries.

Don’t Miss These Must-Read Articles of the Week

Revolutionizing Cybersecurity: Adopting a Risk-Focused Approach in the AI Era

Top Articles You Can’t Miss

Even minor vulnerabilities can lead to severe consequences if not addressed, causing legal and reputational damage. To maintain credibility and prevent breaches, a strategic approach focusing on risk identification and management is essential.AI advancement that necessitates a shift from a vulnerability-focused to a risk-centric cybersecurity strategy. Organizations should evaluate risks based on their broader impact and likelihood, integrating AI for a comprehensive view. AI platforms can aggregate data and prioritize risks by business impact, enhancing efficiency.AI also aids in communicating risks to executives with clear, quantifiable insights, fosters collaboration between security and development teams, and automates remediation. As AI reshapes cybersecurity, leveraging these tools is vital for staying ahead of threats and securing digital assets.

More than 500 AI Models Run Optimized on Intel Core Ultra Processors

Intel has announced that over 500 AI models are now optimized for its Intel Core Ultra processors, which are the leading AI PC processors in the market. This milestone highlights Intel’s significant investment in AI for client devices, optimizing AI models for enhanced performance across CPUs, GPUs, and NPUs. The models cover a wide range of AI tasks, such as image classification and language processing, available from sources like OpenVINO Model Zoo and Hugging Face. This achievement reflects Intel’s commitment to advancing AI capabilities in PCs, facilitating the development of diverse AI features, and improving overall system performance.

Humanoid Robots And Their Potential Impact On the Future of Work

The humanoid robot market, valued at $1.8 billion in 2023, is expected to surpass $13 billion in five years. Tesla’s Optimus robot and the OpenAI-Figure partnership highlight the growing interest in this field. Humanoid robots, designed to mimic human movements, are advancing from science fiction to reality. They face challenges like achieving versatile movements and integrating AI for tasks such as healthcare, education, and customer service. Innovations include improved AI for natural language and vision, enhancing robot capabilities. Future trends involve expanding applications across industries and integrating with technologies including IoT and 5G for better efficiency.

Top AI Insights: Quotes of the Week

“There are many uses for generative AI in manufacturing, ranging from keeping equipment healthy to supply chain optimization. “ –Jim Rowan, AI market activation Leader and Principal, Deloitte

 “One of the most critical parts of ethical AI is maintaining the privacy and integrity of the data used forAI applications.“ –Dr. Arun Gururajan, Vice President, Research & Data Science, NetApp

This Week’s Must-Listen AI Podcast – AI Inspired Series

———————–

A chat with Bradley Jenkins, Intel’s EMEA lead for AI PC & ISV strategies

Bradley talks about the key benefits of Intel’s Core Ultra processor range and how modern enterprises can benefit from systems powered by it.

Listen Now: AI Inspired Series by AiThority.com: Featuring Bradley Jenkins, Intel’s EMEA lead for AI PC & ISV strategies

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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New Enhancements Further Strengthen Performance, Scalability, Stability, and Reporting Power for Blackbaud Impact Edge Customers

Blackbaud, one of the leading providers of software for powering social impact, today announced the integration of multiple Microsoft products – including Microsoft Azure OpenAI Service and Microsoft Power BI – into Blackbaud Impact Edge, the company’s new data and storytelling solution that’s currently under development. The enhancements to the product architecture through Azure OpenAI Service and enhanced reporting through Power BI will enable Blackbaud Impact Edge corporate customers to rapidly produce more contextualized reporting on their social impact programs. Additionally, the company announced that Blackbaud Impact Edge will include the first release to a corporate audience of Blackbaud’s generative-AI chatbot, Blackbaud Copilot.

Also Read: Building a Content Supply Chain in the Era of Generative AI

Blackbaud Impact Edge enables corporate impact professionals to easily monitor, report on, and articulate the various aspects of their organization’s social impact initiatives. The software brings together an organization’s impact data spanning employee engagement programs, philanthropic programs, and educational initiatives—incorporating rich data from both YourCause from Blackbaud CSR solutions and EVERFI from Blackbaud community education initiatives—along with other trusted external data sources. Whether an organization needs to produce a global, unified view of their impact or dive into a granular local community initiative, Impact Edge instantly delivers data, insights, and AI-powered narratives, with best-practice security principles built in.

In collaboration with Microsoft, Blackbaud has integrated new features and solutions into Impact Edge that align with security best practices and expand the robust artificial intelligence capabilities and reporting dashboard.

The integration of Microsoft’s Power BI Embedded platform will enable better data visualization and put key outputs at the user’s fingertips. This integration will also allow Blackbaud to design a more stable and scalable reporting experience within the dashboard while increasing overall performance.

In addition to updates to Impact Edge’s dashboard, Microsoft engineers are supporting the development of key elements of Blackbaud Copilot that is being built into Impact Edge. The features being developed for this version of Blackbaud Copilot will allow Impact Edge to answer questions and generate content based on trusted sources of information, such as the company’s brand voice, information on their website, or other public-facing web domains. With future, more advanced Copilot features, Impact Edge will provide stronger data visualizations, the ability to ingest different formats of data, and improved access to structured data.

“The architecture we are building with Microsoft will help increase overall performance, stability, and scalability for our customers while helping keep their information secure,” said Dale Strange, president and chief operating officer, Corporate Impact, Blackbaud. “Microsoft is a leader in secure and scalable AI capabilities, and their strategic consultation and support have allowed us to build out the cutting-edge reporting and AI features of Impact Edge to produce this incredibly powerful platform that the industry has never seen before.”

Also Read: AI and Big Data Governance: Challenges and Top Benefits

“Blackbaud has been using Microsoft services in an innovative, intelligent way,” said Eduardo Kassner, chief data & AI officer, Software & Digital Platform, Microsoft. “With the support of our engineering teams, Blackbaud has designed their architecture to enable a sophisticated generative AI, data access, and visualization experience, using Microsoft Azure OpenAI Service and Power BI solutions.”

Blackbaud is preparing for the next phase in the roll out of Blackbaud Impact Edge for Corporate Impact customers following a successful early adopter program. Limited availability of Blackbaud Impact Edge has begun with select customers. All features will continue to be enhanced and improved throughout limited availability, plus new features will be added, like data alignment to industry standards such as the United Nations Sustainable Development Goals, the Community Reinvestment Act, and other globally relevant standards, as well as the ability to input and manage toward program goals. Blackbaud Impact Edge general availability is scheduled for the fourth quarter of 2024.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

The post Blackbaud Boosts AI and Reporting via Microsoft Azure and Power BI appeared first on AiThority.

This investment coincides with increased demand for AI-powered multilingual customer support and Language I/O’s go-to-market success.

Language I/O (LIO), a leader in AI-powered, multilingual customer support technology, announced a $5M fundraise led by Gutbrain Ventures, a Venture Capital firm based in Boston, MA. The fundraise included participation from several new venture firms inspired by Language I/O’s unique platform.

Also Read: AiThority Interview with Nicole Janssen, Co-Founder and Co-CEO of AltaML

Bob Davoli, General Partner of Gutbrain Ventures and current Language I/O investor, led the original Series Seed in addition to this funding. “Because this round was led by an existing investor, it presented less distraction, allowing the company to focus and execute on their AI roadmap,” Davoli said. “It also adds fuel to recent revenue growth to further scale their go-to-market team.”

Language I/O CEO Heather Morgan Shoemaker agreed, “We are seeing rapid growth and didn’t want to slow down to focus on fundraising. This success has been fueled by increased demand for AI to automate multilingual customer support, and we have been the vendor of choice for some of the world’s most recognizable brands due to our industry-leading security and efficiency.”

According to a recent study by Market Future Research, the translation service market is expected to reach $47.21 billion at a 2.60% CAGR by 2030 due to growth in multilingual translation technology. In addition, the chatbot market is currently valued at  $5.39 billion and is slated to reach $42.83 billion by 2033, according to Spherical Insights.

Through the utilization of proprietary machine learning (ML)  and generative AI, Language I/O continues to improve the accuracy and efficiency of its real-time translation platform while simultaneously funding R&D efforts in the multilingual chatbot space.

Also Read: Proactive Ways to Skill Up for AI

Key participants in this round also included the State of Wyoming’s Venture Capital fund. Headquartered in Wyoming, Language I/O was the new fund’s first investment last year. Also participating in this round were Joint Effects, PBJ Capital, Omega Venture Partners and the Raptor Group.

One of the new venture capitalist investors, Ken Lang, said he was excited about the space where Language I/O operates. “I’m pleased to be a new Language I/O investor. I was attracted to their strong management team, ability to deliver secure customer support conversations in any language through AI, world-class customers who continue to expand their investment year over year, and compelling product vision.”

Also Read: Red Teaming is Crucial for Successful AI Integration and Application

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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• New platform empowers telecom operators with a customer-centric, self-managed data science workbench to maximize customer lifetime value for both B2C and B2B businesses.

• Offers a library of over a hundred ready-to-use AI model frameworks designed to support CSPs in customer value management (CVM) and revenue growth programs.

Comviva, the global leader in customer experience and data monetization solutions, today announced the release of an all-new no-code AI Workbench for its flagship MobiLytix Marketing Studio.

MobiLytix AI^x empowers businesses and marketers to independently initiate AI-based customer value management (CVM) initiatives in both B2C and B2B areas. With its latest enhancements, Comviva offers a library of over a hundred ready-to-use AI model frameworks specifically designed for marketing and revenue growth programs. These frameworks support CSPs in customer value management, sales, CRM, and OM/SM areas.

Also Read: Niva, Backed by Gradient, Google’s AI Fund, Emerges to Tackle Global Business Verification

The platform has been instrumental in delivering many key use cases such as churn prediction, next-best offers, AI-driven cross-sell/upsell, send-time optimization, customer profile enrichment, customer care, order fallout prediction, fulfillment lead time prediction and sales forecasting.

Available in a SaaS model on hyperscalers, MobiLytix AI allows quick access without extensive installation or configuration. The new solution also includes APIs for connecting with industry-standard data platforms. Integrated with comprehensive security features, it allows CSPs to safely store their data, whether on-prem or in the cloud. Additionally, the platform now features a GenAI-driven chatbot for enhanced user experience and product assistance.

Commenting on the launch Amit Sanyal, Executive Vice President and Chief Operating Officer, MarTech Solutions at Comviva said, “With our new no-code AI workbench, we deliver ready-built solutions for faster time-to-market and increased returns from Customer Value Management (CVM) programs. MobiLytix AI^x empowers telecom operators with a customer-centric, self-managed data science interface for maximizing customer lifetime value for B2C and B2B businesses. The new solution shall be a game changer for the evolving needs of CSPs by significantly accelerating real-time customer campaign delivery.”

Also Read: Quali Uses AI to Simplify Infrastructure as Code and Automate Application Environment Orchestration

MobiLytix AI^x provides insights, automation, and data management, enabling ModelOps/AIOps. It now includes an inbuilt MLOps platform for AI/ML model deployment at the click of a button. Utilizing a user-centered self-service design, it delivers a multi-experience AI development workbench.

Also Read: Revolutionizing Customer Interactions: Introducing Converse AI by Qwary

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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The post Sisense Launches GenAI Analytics Chatbot and Enhanced Narration to Democratize Data Insights appeared first on AiThority.

With the introduction of AI-Driven SQL, BI, and ML assistants, Cloudera builds on commitment to deepen AI capabilities

New features will be on display at booth #537 during the AWS Washington, D.C. Summit

Cloudera, the true hybrid platform for data, analytics and AI, announced three new AI-driven assistants that empower customers to accelerate the development of data, analytics and AI business applications and gain more valuable data analysis. With these new capabilities, Cloudera expands its AI expertise and capabilities for customers to transform data anywhere into actionable insights.

Read: AI in Content Creation: Top 25 AI Tools

While the rise of GenAI has created a dramatic surge in visibility and adoption, enterprises are struggling to develop data, analytics and AI business applications quickly and translate AI’s potential into measurable business impact. With Cloudera’s new AI assistants, enterprises can get trusted data, analytics and AI applications into production faster than ever, while also expanding the scope of employees who can access these tools. This powerful combination enables enterprises to drive AI initiatives more quickly across the entire business and empowers more users to leverage data-driven insights in their everyday roles.

Every company wants to take advantage of the promise of AI, but the models are only as good as the data that they’ve been trained on. According to Gartner, “through 2026, GenAI will reduce manually intensive data management costs by up to 20% each year while enabling four times as many new use cases.” But, we believe that enterprises need the right support to take advantage of this opportunity.

Read: AI In Marketing: Why GenAI Should Be in All 2024 Marketing Plans?

Cloudera’s three new AI assistants are as follows:

• SQL AI Assistant: Cloudera’s SQL AI assistant is built to solve the challenges commonly associated with writing complex SQL queries and enables users throughout the organization to become SQL experts. With the SQL AI assistant, users simply describe what they need in plain language, and the assistant will find the relevant data using advanced techniques like prompt engineering and retrieval augmented generation (RAG). From there, the assistant will write and optimize the query and even explain it back in easy-to-understand terms. By removing the challenge of navigating SQL syntax, the SQL AI assistant empowers enterprises to focus on what really matters – getting insights from their data.
• AI Chatbot in Cloudera Data Visualization: Cloudera’s AI Chatbot within Cloudera Data Visualization is designed to directly converse with enterprise data to provide contextualized business insights that go beyond what BI dashboards can typically display. The chatbot resides directly in Cloudera’s dashboard and reports that are created with Cloudera Data Visualization and leverages the context of the available data behind the dashboard to deliver deeper, more actionable insights to enterprises. Users can simply ask questions in plain language, and the AI chatbot will intelligently match it to the relevant data and visuals.
• Cloudera Copilot for Cloudera Machine Learning: Cloudera Copilot is powered by pre-trained LLMs and are designed to help navigate the challenges typically related to deploying AI and ML models in production, from data wrangling to coding. With seamless integration to over 130 Hugging Face models and datasets, Cloudera Copilot supports the end-to-end development process of AI applications and accelerates data science, model development, and fine tuning. This enables enterprises to accelerate machine learning development and start seeing business value more quickly.

“The AI and ML technological gold rush has brought limitless potential for enterprises, but we’re still seeing significant growing pains across industries when it comes to driving valuable insights from AI,” said Andrew Brust, Blue Badge Insights. “For organizations to capitalize fully on this new frontier, enterprises need to effectively harness AI for business applications. That’s where Cloudera excels, especially as the company continues to infuse AI throughout the platform.”

Earlier this month, the company announced the acquisition of Verta’s Operational AI Platform, further solidifying Cloudera’s AI and ML leadership. By embedding AI assistants for SQL, BI, and ML directly into the platform, Cloudera is unveiling its next phase of enterprise AI for customers by simplifying and enhancing the AI and ML experience for every user.

“As AI advances, Cloudera is committed to staying ahead of our customer’s needs,” said Dipto Chakravarty, Chief Product Officer at Cloudera. “Our new AI-driven assistants eliminate barriers, enabling every user to harness the power of AI across their organizations.”

Cloudera will be demonstrating its latest series of updates at booth #537 at the AWS Washington, D.C. Summit, taking place June 26-27, 2024, at the Walter E. Washington Convention Center. Attendees will see the power of Cloudera’s AI Assistants firsthand and speak to experts onsite about how Cloudera can turn AI hype into business reality.

Read: 10 AI In Manufacturing Trends To Look Out For In 2024

[To share your insights with us as part of editorial or sponsored content, please write to psen@martechseries.com ]

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Nearly three million industrial robots are currently employed in factories all over the world, as reported by the International Federation of Robots (IFR).

Understanding Robotics And AI

As more and more sectors come to rely on them, robots are moving from the realm of science fiction and the hyperactive imagination to the mainstream.

Robotics is a subfield of AI that focuses on the design and implementation of autonomous robots and other intelligent machines. Robotics is interdisciplinary since robots are mechanically constructed, include electrical components, and are controlled by computer code.

Although robots and AI are aimed at separate problems and have diverse applications, the two are often discussed as if they were the same. Robots have a human-like appearance and, with the help of artificial intelligence, can mimic human behavior.

When We Talk About “AI Robots,” What Exactly Do We Mean?

Sensors (such as 2D/3D cameras, vibration sensors, proximity sensors, accelerometers, and other environmental sensors) are added to AI-powered robots to provide them with sensory data on which they may make decisions in real-time.

Read the latest blogs: Navigating The Reality Spectrum: Understanding VR, AR, and MR

Intelligent robots powered by AI bridge the gap between the two fields. AI robots are piloted by computer programs that employ various forms of artificial intelligence (ML, CN, RL, etc.). The vast majority of robots now in use are not AI robots; rather, they are programmable machines designed to carry out specific, repetitive motions. These robots, however, have restricted capabilities.

If you want the robot to be capable of more complicated jobs, artificial intelligence algorithms are a must.

One possible method for a warehouse robot to make its way around is through the use of a path-finding algorithm. When a drone’s battery is low, it may utilize built-in directions to return to base automatically. Possible artificial intelligence algorithms used by a self-driving automobile to identify and sidestep road dangers. These robots are all good instances of those with artificial intelligence.

Benefits Of Incorporating AI Into Robotics

• Artificially intelligent robots’ primary value is in providing social care. With chatbot-like social abilities and powerful processors, they can help guide people and are especially useful for assisting the elderly.
• Another area of robotics application is in agriculture. The use of these robots eases the burden on farmers.
• Military bots can replace troops and save lives while also spying on the enemy undetected via voice and vision detectors.
• Robots are used in environments where people would perish, such as inactive volcanoes, the depths of the ocean, extreme cold, and outer space.
• With a set of instructions and some extra Intelligence, robots can execute difficult procedures that carry a larger risk of error in the hands of humans. There may be many fewer casualties if robots were enhanced with AI.
• Faster product delivery, increased productivity, and more time for humans to focus on value-added activities like process improvement, AMR problem-solving, and creative problem-solving are all the results of this development.
• Artificial intelligence robots can do in-line quality checks in manufacturing settings, eliminating the need to wait until the conclusion of the production cycle.
• Because AI-enabled robots can pick up on cues from human speech and body language, they can get better at their jobs as they securely assist humans.
• Robotics is often used by huge corporations with even larger warehouses since it reduces operating time and intermediate expenses.
• They might greatly enhance our living conditions in places like the classroom and the household.
• Humanoid robots are helping to accelerate the trend toward individualized education.
• Robots that are linked to the internet at home may conduct things like sweeping and cooking for their owners.
• They hold great promise for addressing global issues including climate change, energy scarcity, and healthcare disparities.
• They can be programmed to undertake many of the mundane, repetitive duties that eat up our day so that we have more mental bandwidth for more interesting activities.
• They give us fresh resources for figuring out how to preserve our natural environment for the benefit of present and future generations.
• They give us hope for a society where everyone, regardless of who they are or where they come from, gets a fair shot at realizing their full potential.

Read: AI and Machine Learning Are Changing Business Forever

FAQ’s: AI And Robotics

• Will automation by AI robots make money and power obsolete in the future?

Money wouldn’t only be worthless; it’d be immoral. There would be no way to make money under such a regime. In any plausible scenario, the notion that “more jobs would be created” would have been phased out long before now.

• Will automation generate more job opportunities contrary to general belief?

On the one hand, automation often generates a comparable number of new employment to those it eliminates. Workers who are trained to use machines are more productive than those who are not; this drives down the price of products and services and gives the impression of prosperity to the general public.

• What computer technologies are expected to shape the future of home automation and robotics?

One of the most notable developments in smart home technology is the proliferation of AI-powered home systems. Because of the machine learning feature of AI, it can study sensor data and anticipate what needs to be done before the homeowner ever gives the instruction.

• What factors do clients consider while employing RPA?

Clients may turn to RPA to speed up and improve service in response to consumer demands in specific circumstances. Automation of tracking and reporting activities is one way in which RPA may assist businesses in ensuring regulatory compliance.

• What sort of advances in human-robot collaboration might there be by 2050?

Households will increasingly have personal robots to help with chores like cleaning, cooking, and caring for youngsters and the elderly. Advanced AI will be built into these robots, expanding their capabilities and making them useful in a variety of settings.

The Perfect Match

Statistics show that just one percent of robotics firms are successful; nonetheless, that one percent is increasingly exerting global dominance.

Without a doubt, robotics and AI have the potential to revolutionize our society, giving us the tools we need to confidently and optimistically face an uncertain future.

Whether we welcome or reject these new forms of technology, they will certainly continue to play an ever-greater part in our lives.

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Synthetic Data

Modern chatbots rely on large language models (LLMs) that have been pre-trained on raw text to acquire an abstract understanding of language. This sets them up for rapid task acquisition after seeing detailed, labeled instructions during alignment. However, reliable instructional data is not easily accessible. Humans can’t afford to make it, and it usually doesn’t have the breadth and depth that chatbots require to handle uncommon, complex, or hard situations. Although synthetic data is significantly more affordable, it frequently has the same problem of being monotonous.

Read: Optimizing IBM’s Cloud-Native AI Supercomputer for Superior Performance

LLM Developers

• LLM developers may specify the information and abilities they wish to imbue their chatbot with using IBM’s taxonomy-driven data-generation approach. To help developers find and fill in knowledge gaps, the taxonomy organizes the LLM’s current abilities and knowledge in a systematic, hierarchical fashion.
• A second LLM, the teacher model, uses the taxonomy to generate task-specific question-and-answer pairs that are considered high-quality instructions. Consider the following scenario: you would like a chatbot to compose an email outlining the company’s third-quarter financials and send it to the CEO. The ideal candidate will have experience with financial statements, be proficient in basic arithmetic and reasoning, and have the ability to concisely and persuasively describe financial facts in an email. The LLM developer may begin this made-up scenario by uploading the company’s financial accounts together with multiple sample calculations for corporate earnings. The financial records would serve as the basis for the instructions generated by the teacher model. In that manner, new instructions can be made if accounting regulations change. Another way is for the teacher model to tell the base LLM how to figure the earnings.
• The third option involves the developer providing example earnings-report emails, which the teacher model then uses to train the base model to compose the desired email.

Read 10 AI In Manufacturing Trends To Look Out For In 2024

Large-scale Alignment for chatbots (LAB)

LAB was also instrumental in helping IBM refine its own Granite models on IBM Watson with an eye toward enterprise application. Large-scale Alignment for chatbots (LAB) is IBM’s latest offering in this space. It’s a way to construct synthetic data for the jobs you want your chatbot to do and to incorporate new skills and knowledge into the base model without erasing its previous learnings. Training LLMs normally take a lot of time and money, but with LAB, LLMs can be significantly enhanced with much less effort and expense.

Read: Top 15 AI Trends In 5G Technology

Knowledge, foundational skills, and compositional skills that build on knowledge and foundational skills are the three main categories into which instruction data is separated according to IBM’s taxonomy. Knowledge of accounting, proficiency in mathematics, and the ability to write and reason coherently are all possible pieces of information that might be required in this case. The instructor model would iteratively conduct quality control on its outcomes while generating instructions for each category. The data that the instructor model produced is also subjected to quality control assessments. It eliminates irrelevant inquiries and directions with inaccurate information by being its own harshest critic. The figure below has been taken from the website of IBM.

The approved instructions are then divided into three sections: knowledge, basic skills, and compositional skills. This allows the LLM to process them in two steps. Just like humans learn new things by building on what they already know, the LLM may do the same through its tiered training schedule. Labradorite 13B (based on Meta’s Llama-2-13B model) and Merlinite 7B (based on the Mistral 7B model) were trained using a synthetic dataset of 1.2 million instructions that IBM Research created using the LAB approach. Their aligned models outperformed state-of-the-art chatbots on several tests, including those measuring natural language understanding and conversational fluency.

Chatbots educated on massive amounts of synthetic data, such as Microsoft’s Orca-2 chatbot—trained on fifteen million instructions created by the GPT-4 model—lacked the performance of IBM’s Labradorite and Merlinite models. These outcomes can be better understood because of two characteristics of LAB. A considerably wider range of target tasks is produced by the teacher model, which generates synthetic examples from every taxonomy leaf node. Alternative approaches rely on random sampling, severely restricting the data’s breadth.

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Full compensation, albeit a modest return on investment, will be provided to Inflection’s investors through the deal with Microsoft. Despite this, the previously unrealistically high value of Inflection at $4 billion after a $1.3 billion funding round last year means that these investors will not see any immediate large returns.

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Inflection AI and Microsoft

Last year, Inflection debuted Pi, a chatbot that was marketed as a better personal assistant than its rivals, during a time when chatbots were receiving a lot of attention from investors. Inflection had trouble developing a sustainable economic model. Venture capitalist and Microsoft board member Reid Hoffman will continue to be actively involved with Inflection as a director and co-founder while the company navigates its future.

Also, Inflection’s patented technology will remain in its hands. The firm positioned itself as a top AI platform for companies around the world claiming that it is prepared to work with firms like Microsoft and showcasing its skills in training, personalizing, and improving large-scale AI models. The nontraditional arrangement is similar to an “acqui-hire” concept, except that no acquisition is happening. Authorities have recently ramped up their investigation into the investments and partnerships of big tech companies including artificial intelligence.

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Training AI Models

Inflection AI is currently looking into ways to sell off its surplus computer capacity since the company operates with a drastically reduced workforce (SCMP). Included in this category are the computing resources that are used for things like training AI models. A partial refund is allegedly being discussed between the company and its cloud computing partner CoreWeave. As Inflection shifts its focus from consumers to enterprises, this move is made to reduce the expenses linked to developing AI models.

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