As an official technology partner of Qlik in AI, Luminoso is excited to seamlessly deliver the power of its technology to Qlik users worldwide.

Luminoso, a leader in AI-driven text analytics, is excited to announce a partnership with Qlik, a global leader for data integration, data analytics and business intelligence solutions. This partnership is set to transform the field of analytics by integrating Luminoso’s advanced text analytics into Qlik’s leading BI tool, providing professionals with unparalleled insights and decision-making capabilities. As an official technology partner of Qlik in AI, Luminoso is excited to seamlessly deliver the power of its technology to Qlik users worldwide.

Business analysts and customer experience professionals who try to analyze their customers and markets solely through the lens of structured data limit their ability to derive the broadest and best sets of insights about customer behavior. In fact, this structured data represents only 20% of enterprise data according to multiple analyst estimates. The remaining 80% of data is unstructured and can contain valuable customer insights through sources such as product reviews, discussion forums, trouble tickets, and customer emails.

By integrating Luminoso’s ability to understand unstructured data and structured consumer sentiment with Qlik’s depth of analytics, companies are now able to analyze and understand previously disparate data sources and types to gain a more complete view and thus achieve a competitive advantage.

“Structured data can point to a decline in sales that’s already happened. Understanding and tracking customer sentiment through unstructured data can serve as a leading indicator of financial performance but also can help shape future product direction and the entire customer experience. This is why we believe the combined capabilities of Luminoso and Qlik in a single environment are profoundly compelling,” said Mark Zides, CEO & President of Luminoso.

Also Read: AiThority Interview with Seema Verma, EVP and GM, Oracle Health and Life Sciences

“Luminoso deciphers and quantifies human feedback. Luminoso then integrates its results along with its visual analytics within Qlik Sense^® for a new level of insight that was previously not available. This combination will enable comprehensive customer & market intelligence, providing an all-round understanding of consumer sentiments and preferences ” said Hugo Sheng, Senior Director of Partner Engineering.

Luminoso’s solutions were spotlighted at Qlik Connect 2024, where we engaged with hundreds of global business leaders & analysts from HR, Retail, technology, healthcare, and marketing sectors.

“This integration allows for users to perform sentiment analysis and trend identification on a concept level within Qlik dashboards. This enhanced analysis helps businesses make informed decisions, optimize strategies, and drive better outcomes,” said Dalton Ruer, Senior Solutions Architect – Partner Engineering at Qlik.

Announcing Prathik N Sunku as Head of Partnerships & Alliances and David Rautkys as Head of Corporate Development & Innovation

“We are thrilled to announce the appointments of Prathik N Sunku as Luminoso’s new Head of Partnerships & Alliances, and David Rautkys as Head of Corporate Development & Innovation. Prathik brings a wealth of experience in fostering strategic partnerships and driving growth in the analytics and technology sectors, while David’s expertise in innovation and corporate development will be instrumental in expanding Luminoso’s reach and enhancing its impact in the market,” said Mark Zides.

​​”Our AI’s ability to deliver in-depth, nuanced comprehension allows businesses to quickly familiarize themselves with customer and market dynamics by analyzing real-time feedback from platforms like Reddit, Amazon, and Glassdoor, directly within Qlik dashboards,” said Prathik N Sunku.

“Analysts and consultants can explore new dimensions using feedback data organized as structured insights from discussions and conversations across different channels, all within Qlik workflows. This integration offers sales teams deeper insights into customer behavior and preferences, enabling more targeted and effective sales strategies,” added David Rautkys.

Also Read: AiThority Interview with Kunal Purohit, President – Next Gen Services, Tech Mahindra

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Accelerating GenAI Adoption with an Open Source Platform for Seamless AI, LLMOps, and MLOps Development, Deployment, and Resource Management

ClearML, the leading solution for unleashing AI in the enterprise, today announced the launch of its expansive end-to-end AI Platform, designed to streamline AI adoption and the entire development lifecycle. This unified, open source platform supports every phase of AI development, from lab to production, allowing organizations to leverage any model, dataset, or architecture at scale. ClearML’s platform integrates seamlessly with existing tools, frameworks, and infrastructures, offering unmatched flexibility and control for AI builders and DevOps teams building, training, and deploying models at every scale on any AI infrastructure.

With this release, ClearML becomes the most flexible, wholly agnostic, end-to-end AI platform in the marketplace today in that it is:

– Silicon-agnostic: supporting NVIDIA, AMD, Intel, ARM, and other GPUs
– Cloud-agnostic: supporting Azure, AWS, GCP, Genesis Cloud, and others, as well as multi-cloud
– Vendor-agnostic: supporting the most popular AI and machine learning frameworks, libraries, and tools, such as PyTorch, Keras, Jupyter Notebooks, and others
– Completely modular: Customers can use the full platform alone or integrate it with their existing AI/ML frameworks and tools such as Grafana, Slurm, MLflow, Sagemaker, and others to address GenAI, LLMOps, and MLOps use cases and to maximize existing investments.

“ClearML’s end-to-end AI platform is crucial for organizations looking to streamline their AI operations, reduce costs, and enhance innovation – while safeguarding their competitive edge and future-proofing their AI investments by using our completely cloud-, vendor-, and silicon- agnostic platform,” said Moses Guttmann, Co-founder and CEO of ClearML. “By providing a comprehensive, flexible, and secure solution, ClearML empowers teams to build, train, and deploy AI applications more efficiently, ultimately driving better business outcomes and faster time to production at scale.”

The ClearML end-to-end AI Platform encompasses newly expanded capabilities and integrates previous stand-alone products, and includes:

Also Read: AiThority Interview with Seema Verma, EVP and GM, Oracle Health and Life Sciences

– A GenAI App Engine, designed to make it easy for AI teams to build and deploy GenAI applications, maximizing the potential and the value of their LLMs.
– An Open Source AI Development Center, which offers collaborative experiment management, powerful orchestration, easy-to-build data stores, and one-click model deployment. Users can develop their ML code and automation with ease, ensuring their work is reproducible and scalable.
– An AI Infrastructure Control Plane, helping customers manage, orchestrate, and schedule GPU compute resources effortlessly, whether on-premise, in the cloud, or in hybrid environments. These new capabilities, which were also introduced today in a separate announcement, maximize GPU utilization and provide fractional GPUs, as well as multi-tenancy and extensive b****** and chargeback capabilities that offer precise cost control, empowering customers to optimize their compute resources efficiently.

ClearML’s AI Platform enables customers to use any type of machine learning, deep learning, or large language model (LLM) with any dataset, in any architecture, at scale. AI Builders can seamlessly develop their ML code and automation, ensuring their work is reproducible and scalable. That’s important, because it addresses several critical challenges faced by organizations in developing, deploying, and managing AI solutions in the most complex and demanding environments. Here’s why it matters:

Unified End-to-end Workflow: ClearML provides a seamless workflow that integrates all stages of AI development, from data ingestion and model training to deployment and monitoring. This unified approach eliminates the need for multiple disjointed tools, simplifying the AI adoption and development process.

Superior Efficiency and ROI: ClearML’s new AI infrastructure orchestration and management capabilities help customers execute 10X more AI and HPC workloads on their existing infrastructure.

Interoperability: The platform is designed to work with any machine learning framework, dataset, or infrastructure, whether on-premise, in the cloud, or in a hybrid environment. This flexibility ensures that organizations can use their preferred tools and avoid vendor lock-in.

Orchestration and Automation: ClearML automates many aspects of AI development, such as data preprocessing, model training, and pipeline management. This ensures full utilization of compute resources for multi-instance GPUs and job scheduling, prioritization, and quotas. ClearML empowers team members to schedule resources on their own with a simple and unified interface, enabling them to self-serve with more automation and greater reproducibility.

Scalable Solutions: The platform supports scalable compute resources, enabling organizations to handle large datasets and complex models efficiently. This scalability is crucial for keeping up with the growing demands of AI applications.

Optimized Resource Utilization: By providing detailed insights and controls over compute resource allocation, ClearML helps organizations maximize their GPU and cloud resource utilization. This optimization leads to significant cost savings and prevents resource wastage.

Budget and Policy Control: ClearML offers tools for managing cloud compute budgets, including autoscalers and spillover features. These tools help organizations predict and control their monthly cloud expenses, ensuring cost-effectiveness, by providing advanced user management for superior quota/over-quota management, priority, and granular control of compute resources allocation policies.

Enterprise-Grade Security: The platform includes robust security features such as role-based access control, SSO authentication, and LDAP integration. These features ensure that data, models, and compute resources are securely managed and accessible only to authorized users.

Real-Time Collaboration: The platform facilitates real-time collaboration among team members, allowing them to share data, models, and insights effectively. This collaborative environment fosters innovation and accelerates the development process.

Also Read: AiThority Interview with Kunal Purohit, President – Next Gen Services, Tech Mahindra

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Artificial intelligence is revolutionizing software development at an unprecedented pace. AI coding tools are unlocking new possibilities, enabling developers to ideate, create, and iterate with remarkable speed. This rapid advancement raises pertinent questions: Can AI write code? Can AI coding tools assist in learning to code? More crucially, does AI pose a threat to the future of software engineering by potentially replacing human programmers?

Contrary to these concerns, the future of software engineering remains secure. AI tools are not job-destroyers but valuable additions to a programmer’s toolkit. They enhance efficiency and creativity without rendering human expertise obsolete. As we explore the capabilities and implications of AI in coding, it becomes evident that these tools are more boon than threat, augmenting rather than replacing the role of the software engineer.

AI is now embedded in many activities today, from streaming television entertainment to finding products online. In coding, AI automates tedious processes and assists developers in tackling complex troubleshooting problems.

Developers use AI for various tasks, from marketing integration tools to customer-facing software applications. By 2023, 92% of U.S. coders reported using AI tools, and 70% claimed these tools improved their work (GitHub). The widespread adoption of AI coding tools indicates a significant shift in the industry.

Also Read: Conversational AI Is Here to Stay, but Don’t Overlook the Risks Before Basking in the Rewards

What are AI Coding Assistants?

AI coding assistants are tools powered by machine learning algorithms designed to enhance the coding process. They provide developers with intelligent code completion, generate code snippets, and automate repetitive tasks. By offering context-aware suggestions and autocompletion, these assistants significantly speed up coding and reduce developers’ cognitive load, making coding faster and more efficient.

However, their capabilities extend beyond basic autocompletion. Leading AI coding tools offer features such as:

• Text-to-code generation from natural language descriptions
• Automatic bug detection and fix suggestions
• Code refactoring recommendations
• Language translation (converting code from one programming language to another)
• Real-time code explanations and documentation generation

The post AI Coding Tools: Are They a Threat or a Boon for Coders? appeared first on AiThority.

Advantages of IoT and AI Synergy in the Telecom Sector

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) offers transformative benefits across various industries. In the telecom sector, the synergy between these technologies can drive significant improvements in operational efficiency, customer experience, and overall service quality. Here are the key advantages:

1. Autonomous Network Management AI enables IoT devices to manage and optimize telecom networks autonomously. By analyzing real-time data from network sensors, AI can predict and resolve issues without human intervention, ensuring consistent service quality and reducing downtime.
2. Enhanced Data Analytics Telecom networks generate vast amounts of data. AI-powered analytics can process this data to uncover patterns, trends, and anomalies that traditional methods might miss. This leads to more accurate demand forecasting and better network capacity planning.
3. Operational Efficiency Integrating AI with IoT in telecom operations allows for predictive maintenance of network infrastructure. AI can identify potential equipment failures before they occur, reducing unplanned outages and maintenance costs.
4. Personalized Customer Experiences AI uses data from IoT-enabled devices to gain insights into customer behavior and preferences. Telecom providers can leverage these insights to offer personalized services, targeted promotions, and tailored customer support, enhancing customer satisfaction and loyalty.
5. Network Optimization AI algorithms can analyze data from IoT sensors to optimize network performance dynamically. This includes adjusting bandwidth allocation, load balancing, and traffic management, resulting in a more efficient and reliable network.
6. Energy Efficiency IoT devices monitor energy consumption across telecom facilities. AI analyzes this data to optimize energy use, reduce operational costs, and promote sustainable practices. This is particularly important for telecom companies looking to minimize their environmental impact.
7. Smart Infrastructure Management IoT sensors collect data on the condition of telecom infrastructure, such as cell towers and data centers. AI processes this data to optimize maintenance schedules, improve asset utilization, and extend the lifespan of critical infrastructure.
8. Fraud Detection and Prevention AI can analyze data from IoT devices to detect unusual patterns that may indicate fraudulent activities. By identifying and responding to these threats in real time, telecom providers can protect their networks and customers from potential fraud.
9. Improved Supply Chain Management In the telecom sector, IoT-enabled devices provide real-time tracking of equipment and inventory. AI analyzes this data to streamline logistics, optimize supply chain operations, and reduce delays, ensuring timely delivery of services and products.

Challenges of AI and IoT Integration in the Telecom Sector

Integrating Artificial Intelligence (AI) and the Internet of Things (IoT) in the telecom sector presents numerous opportunities, but it also comes with significant challenges. These challenges must be addressed to realize the full potential of this technological synergy.

1. Data Privacy The integration of AI and IoT generates vast amounts of data, much of which is sensitive. Ensuring the privacy and security of this data is crucial. Telecom companies must implement robust data protection measures to comply with regulatory requirements and maintain customer trust.
2. Integration Complexity Combining AI with IoT in the telecom sector is a complex task. It requires developing a robust infrastructure, employing a skilled workforce, and meticulous planning. The integration involves navigating challenges related to hardware compatibility, software development, and system interoperability, demanding significant effort and coordination.
3. Ethical and Societal Implications The deployment of AI and IoT technologies raises ethical and societal concerns. Issues such as the ethical use of AI in decision-making processes and the potential for job displacement due to automation need careful consideration. Responsible development and use of these technologies are essential to mitigate negative societal impacts.
4. Cybersecurity In an interconnected world, cybersecurity is a fundamental concern. Protecting IoT devices and AI systems from cyber threats is an ongoing challenge. AI can both enhance security and be exploited by cybercriminals. Telecom companies must continuously update their cybersecurity measures to stay ahead of potential threats.
5. Standards and Interoperability Ensuring that AI and IoT devices from different manufacturers can communicate seamlessly is a significant challenge. Establishing common standards and achieving interoperability is critical for the success of AI and IoT integration. This is especially important in the telecom sector, where devices and systems must work together in a cohesive ecosystem.
6. Resource Allocation Properly allocating resources for AI and IoT integration is a delicate balancing act. Telecom companies must weigh the costs of implementation against the anticipated benefits. This financial consideration impacts strategic decision-making at all levels, from startups to established enterprises, influencing the pace and scale of adoption.

AI-Powered Telecom Companies in the World

1. AT&T

2. COLT

3. Deutsche Telekom

4. Globe Telecom

5. Telefonica

6. Vodafone

7. ZBrain Cloud Management

Final Thoughts

Integrating AI and IoT transforms the telecommunications sector by enhancing network performance and delivering personalized services. IoT sensors provide real-time insights into network congestion and equipment failures, while AI algorithms predict outages, optimize bandwidth, and bolster reliability. This synergy also enables IoT-enabled devices to offer tailored plans and promotions, increasing customer satisfaction and loyalty.

Beyond telecommunications, the convergence of AI and IoT is revolutionizing industries such as healthcare and manufacturing and reshaping our daily lives through innovations like smart homes. This integration of advanced technologies enhances efficiency, improves decision-making, and contributes to a safer and more convenient world.

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

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We’re at a point where organizations should not bypass implementing and using AI in some capacity in their operations. The benefits of the technology are too great to overlook in how it can augment employees in their work and solve business use cases in more efficient ways.

Conversational AI chatbots can help simplify and streamline entire processes through the automation of day-to-day activity. Its benefits are unmatched – whether you’re an HR professional who uses this technology to optimize onboarding and recruitment with ease or a call center agent who needs to tap into the right information for customers in seconds.

But this is not a technology that you can implement and then walk away from. To maximize its value and benefits, it requires continuous monitoring and iteration. On top of that, as with any new technology, the risks involved need to be fully realized. Accuracy and security are at the top of the list and if left unchecked, an organization can expose itself to negative reputational or even financial repercussions.

With stakes that high and the presence of conversational AI chatbots increasing by the day, it’s critical for organizations to overcome these hurdles from the start so that they can fully reap the rewards that this technology will bring.

Also Read: AiThority Interview with Dr. Arun Gururajan, Vice President, Research & Data Science, NetApp

Hallucination and reliability issues

Accuracy remains a problem as generative AI continues to proliferate in the market. According to a report from Aporia on AI models, 89% of machine learning engineers say their LLMs exhibit signs of hallucinations, for example.

It’s an issue that’s even made headlines. Google’s new AI Overview feature was found recently to recommend that users add glue to their pizza when baking so that they can get the cheese to stick better. Google claimed the issue was due to an information gap and a misinterpretation of language when searching for results.

For all the incredible capabilities and thoughtful responses that AI can provide, hiccups can and will happen, and the ultimate cause of this problem is improper AI training. There’s much that can be done to fix this, and it starts with training data. The varying levels of training data and algorithms used from model to model is the underlying issue here. Thus, ensuring that AI models are provided with the highest quality data is the key to the chatbot operating as error and bias-free as possible.

Direct human feedback is another solution to this, and it’s also where increased voice functionality is a benefit. With all the added contextual information that voice provides, it’ll become a crucial part of AI model training moving forward. Business leaders expect the tools their organizations use to operate to be accurate, otherwise they risk damage to their reputation and customer base. They can’t afford to throw their weight behind an AI that is putting out inaccurate information alongside all the factual insights it’s generating.

Having to frequently look over the chatbot’s output defeats the purpose of the rapid and informative information it’s capable of providing. If this problem isn’t fixed, it’ll cause serious problems for businesses when it comes to trust from its customers.

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

Data privacy and security

While hallucinations and accuracy remain issues with AI chatbots, recent data shows that it’s not the only problem that’s keeping business leaders up at night. According to a new study from Alteryx, while over three-quarters of organizations said there was business value in using generative AI, 80% listed data privacy and security concerns as their biggest concerns.

With AI rapidly becoming integrated into our daily lives, it’s important that organizations get data security right or risk losing external confidence in their solutions. This underscores the fundamental shift that’s emerging as technology is ingrained in some capacity in every business’s operations and infrastructure.

Security is often the first question that an organization will ask a prospective AI provider about before integrating the technology into their processes. They’ll be particularly interested to understand how the model retains data, if the model is used to train itself, and about data leak risks. This is where the benefits of utilizing a small language model come in handy, where it can be integrated and deployed into an organization’s on-premise environment, so there’s no outside access to the internet required to utilize the full capabilities of an AI chatbot.

Ultimately, any data sharing practices and storage need to comply with stringent privacy regulations, as well as regular security monitoring and data encryption. Failure to meet strict standards can be detrimental as any data breaches can harm a businesses’ public standing and potentially trigger legal or financial penalties.

Also Read: Cybersec Specialist Gareth Russell Joins Commvault as Field CTO, Security for APAC

AI is the key to better productivity

Generative AI use has boomed, and business leaders are seeing the results, as 77% reported running successful AI co-pilots within their organizations. This shows that many organizations are placing their trust in this technology, and are now being rewarded with deployments that are paying dividends for their business solutions.

But it’s important to remember here that AI models benefit from a human touch and being iterated continuously, otherwise if not updated or left unchecked, it can cause serious issues. When this proactivity is fully acted upon, AI will continue to be a tool that enhances processes and helps us in so many different ways. It’s been remarkable to see its use cases and integrations grow across any sector imaginable.

For example, in healthcare we’ve seen conversational AI chatbots transform the digital experience for patients by acting as an efficient virtual assistant for all of their needs. From quickly analyzing user responses to prompts regarding symptoms and risk factors to improving the scheduling process and helping the patients make the right appointment, this technology can improve satisfaction on both sides.

In an HR setting, a conversational AI chatbot can streamline new candidate onboarding and employee retainment. By optimizing the common tasks that make up these processes, it can help HR professionals better identify the right candidates for open positions. When an HR team isn’t swarmed with applications to analyze, they’re freed up to develop stronger relationships with the most promising candidates and keep current employees satisfied.

These examples show how speed, efficiency, and personalization can all be amplified when AI technology is integrated into business solutions. Simply put, the experience for internal and external users is made better and organizations can directly see how technology like a conversational AI chatbot can help them accomplish their objectives in a more productive fashion.

Conversational AI is such a fascinating technology because of how it’s being used to solve business needs that have existed for decades in new and exciting ways. The value it’s creating for organizations in only the last several years has been great to see, and there’s no signs of slowing down. It’s too important of a technology to disregard, and it’s time we all move forward with it.

But we also can’t sit back and let this technology fully take off without taking into account the risks around accuracy and security. Conversational AI chatbots are here to stay, and they’ll only keep improving as they become more skilled at processing requests quicker and even more personalized, like a true personal assistant for every use case that’s needed.

It’s important that all stakeholders truly get this right as this technology continues to expand exponentially so that they can have the greater functionality and peace of mind that it can bring.

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

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Software industry veteran to guide AI security leader through next phase of growth

CalypsoAI, the leader in AI security, has named Donnchadh Casey as its new Chief Executive Officer. He succeeds Founder Neil Serebryany, who will remain a member of the CalypsoAI board.

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

“Since joining, I’ve seen this innovation first-hand and worked directly with the team driving it. In my new role, I’m relishing the opportunity to drive the next phase of growth and bring this innovation to enterprises everywhere.”

“Six years ago when we founded CalypsoAI, the AI security industry didn’t exist—meaning that companies looking to take advantage of this powerful technology were almost immediately putting themselves at risk. We built an industry to address a next-generation threat we knew was coming,” said Serebryany. “We’ve now accomplished that, which leads to a natural transition point. I’m incredibly proud of our team and what we have started together, and I look forward to what’s next with Donnchadh at the helm.”

“When I first learned about CalypsoAI, it was clear that the company was at the forefront of addressing AI security gaps in modern enterprises across the globe,” said Casey. “Since joining, I’ve seen this innovation first-hand and worked directly with the team driving it. In my new role, I’m relishing the opportunity to drive the next phase of growth and bring this innovation to enterprises everywhere.”

Also Read: Humanoid Robots And Their Potential Impact On the Future of Work

Casey currently serves as CalypsoAI’s Chief Operating Officer, bringing nearly 20 years of experience leading global enterprise software brands. Prior to CalypsoAI, he served as the Chief Customer Officer at Qualtrics, a global leader in developing exceptional customer and employee experiences. During that time, he was responsible for ensuring customer success and played a pivotal role in scaling the business to $1.8 billion in annual recurring revenue. Casey’s understanding of how to shape software categories with cutting-edge solutions uniquely positions him to lead the next chapter in CalypsoAI’s growth.

Enterprises worldwide are turning to CalypsoAI for their generative AI (GenAI) security needs. As the only platform using advanced GenAI for comprehensive protection, CalypsoAI sets a new industry standard with seamless integration, robust security, and scalable deployments. The company has experienced explosive growth, driven by high demand for its cutting-edge solutions. This success powered Everest Group’s groundbreaking research and has resulted in numerous accolades, including recognitions from Frost & Sullivan and IDC. With revolutionary features like its GenAI Scanners, CalypsoAI offers a significant leap over legacy techniques, enabling companies to swiftly adapt their security posture to new and emerging threats.

Don’t miss this out: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

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

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In many areas, artificial intelligence is making routine what used to be impossible – and that’s perhaps especially true in business. When users first witness what AI is capable of, they are often in awe – and when they realize they can utilize this technology to accomplish far more than they were capable of previously, they sometimes become overly dependent on it. Bad idea; despite its advanced capabilities, AI still has some serious flaws.

In order to be truly effective, AI needs a human “boss.” In the human-AI partnership, it’s the human who needs to come first – who needs to “lead” the AI system into providing results that make sense, by reviewing and applying experience and logic to the results provided by these amazing tools.

For example, AI can sometimes display “tunnel vision,” unaware of “big picture” policy issues and long-term corporate goals. It’s sort of like a star employee who is very good at their job – but lacks knowledge or awareness of a company’s long-term strategic goals. What you want from that employee is their productivity in their specific area – not an overhaul of the company based on their limited knowledge. Users need to treat AI systems as that “talented employee” – keeping in mind that they need to be in control of the overall project strategy. Just like a talented employee needs mentoring and guidance, so too AI. With that human guidance, companies can extract the greatest value from their new “star performers;” without it, the company could find itself in big trouble. And, when introducing new AI tools, which are increasingly employed to do jobs and tasks, they should be treated just like a new employee. This means that they need a boss to mentor and guide them, and supervise and check their work, at least in the beginning. And with AI, we are still at the beginning; and some micromanagement is often needed.

Also Read: Humanoid Robots And Their Potential Impact On the Future of Work

There’s no question that AI tools have been a major boon to productivity – boosting output by nearly 500%, according to studies – and enabling businesses to become more agile, competitive, and efficient. And now that we’ve gotten used to employing them, doing business without these tools is inconceivable. As time goes on, AI tools will improve even more – and with them, the commensurate benefits in efficiency and profitability businesses will be able to extract from them. Indeed, for better or worse, business research today is often just posing a question to an AI tool.

It is true that humans are learning more about the nuances of this process all the time. For example, it is increasingly understood that by asking the right question, and using the right prompt, most AI tools will give you far better results than you could have gotten manually in such a short period of time. Automated AI systems will parse data in a matter of minutes, if not seconds – far faster than any human could hope to – and present it in a logical manner that is easily understood and comprehended. The temptation to take those results and run with them is, understandably, very strong.

But those automated results need to be understood, reviewed, and checked for accuracy. As smart as it is, AI sometimes comes up short. AI tools sometimes produce inaccurate, incorrect, or even illogical results – and without a human supervising the data generation process, companies could find themselves facing fines, lawsuits, and damaged reputations. Air Canada furnishes a good example of what can happen when AI runs unchecked: The company was recently ordered to pay damages to a passenger who paid full fare for tickets to a grandparent’s funeral, based on incorrect information furnished by an AI-powered chatbot. The company’s defense was that it could not be held responsible for incorrect information furnished by the chatbot – an argument rejected by the court, which ordered Air Canada to refund the overcharge. Had a human reviewed the information offered by the chatbot, the airline could have avoided the expense – and embarrassment – that ensued.

But it’s not just about company coffers: Overreliance on automatically generated AI data can damage or even derail a career. In order to make an effective presentation – whether in person, in a presentation, or in a Zoom meeting – the presenter needs to be intimately familiar with the information they are presenting. This is difficult if the presenter is simply using information produced by AI. For example,  if the automated data is incorrect, they are likely to be called out on it – with the audience or stakeholder demanding to know the source of data, the reasoning behind a statement, or the logic of an argument. And the presenter will likely not be able to answer in an effective and competent manner. A similar situation could arise even if the data is correct—those listening to the presentation could very well start asking follow-up questions, or want to know the source or reasoning behind it.

Also Read: The Ethical Dilemmas of Generative AI

In order to effectively – and safely – utilize their automated results, AI users need to engage in some “active learning,” where they evaluate the results and apply knowledge, facts, and experience to the review process. If the user follows that path, they could ask themselves the same questions likely to be posed to them – giving them time to find the answers they need. But ignoring that review could put them in jeopardy – making them look like fools when presenting information that on the surface appears to be correct, but might be riddled with flaws and or other factors that lead to questions.

It’s a fact that more than half of Americans are concerned about AI’s effects on their lives. Among other things, some fear losing their jobs to AI, some fear AI systems will compromise their privacy, some fear politicization of results. And it’s understandable why people fear AI: It’s been presented in the media as a monolithic, independent “monster” that is going to change life fundamentally, turning us all into its servants, if not destroy us. But that’s not the case: AI is just the latest in advanced tools that we can use to make business, and life, easier and better. We don’t work for AI – it works for us. AI users should keep this in mind when using advanced tools to do their business research. It’s the human user who is in charge, who needs to lead – and the best way to do that is to utilize their experience and knowledge to ensure that the results AI tools provide are accurate, correct, and logical.

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

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AI is a major disrupter, offering great benefits and free labor. However, small companies are being left behind while larger companies are embracing this new tech, writes Christine Telyan

In theory, AI represents a major opportunity for businesses of all sizes to unlock new opportunities and markets and to radically overhaul their operations. In some quarters, AI is being touted as a cure for all business ills. No matter what problem a business has, AI can solve it, enthusiasts say.

But while AI is certainly a disruptive force, we would be mistaken in thinking that its benefits are being distributed evenly. As it stands, AI is simply more useful for experienced teams than it is for solo entrepreneurs or, indeed, Mom-and-Pop type stores and businesses.

Also Read: Humanoid Robots And Their Potential Impact On the Future of Work

Small businesses, the vast majority of which have two employees or fewer, simply don’t have the time, knowledge or expertise to take full advantage of these new AI tools. Yes, theoretically, AI could save them vast amounts of time and enable all sorts of new activities. But many small business owners are so heavily focused on the day-to-day of their companies that learning how to effectively use new software, even a relatively straightforward one like ChatGPT, is more challenging and daunting than people think.

We surveyed our own customers and received over 800 responses. The message on AI was very clear. Only a quarter of small businesses regularly use AI-powered tools. So, despite all the commentary and discussion, 75 percent of small businesses have yet to embrace these new tools—even though many are low cost relative to the time saved. While other studies have shown that small businesses are widely adopting AI, such studies consider small businesses to be companies with up to 100 employees; the experience of such businesses is vastly different from the mom-and-pops we surveyed.

Indeed, for larger businesses, things look rather different. Surveys suggest that roughly 75 percent of medium and large businesses are using AI regularly. These results suggest that, rather than being a leveler and democratizing force, AI is creating a chasm between larger businesses and small ones. As one of my team suggested, AI could stand for ‘Absolute Inequality’, unless small businesses get some help.

Our survey revealed that nearly half (48%) of small business owners work more than 60 hours per week, with more than 73% working over 40 hours weekly. So small businesses are still working very long hours. So why aren’t they employing digital tools to make life easier? We all know tech can be used to schedule meetings and client calls, improve inventory management, track expenses, create targeted marketing and more. But why aren’t small businesses using it?

Well, of course some are, and the 25 percent in our survey who regularly use AI are saving up to five hours per week. But it seems that some need more encouragement, help and, probably, the support of platforms that do more of the heavy lifting for them.

So, what steps can small businesses and organizations take to better embrace AI? Here are some simple steps and questions that I would recommend business leaders to consider.

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

Identify low-hanging fruit

Let’s start where it’s easiest to find success. Looking for simple, repetitive tasks is a good place. Make a list of activities that you do regularly and which eat into your time. Even if you don’t know how to automate these yet, at least be aware of what the problems are. Also, add to the list things you’d like to do more regularly but don’t have the time to do properly. A list of problems might not sound like a good start but these are low-hanging fruit for you to pick later.

Look for help

Don’t suffer alone. Now that you know areas where you can potentially automate, it’s worth asking others how to do this. Engage with small business development centers (SBDCs), business networks and professionals that can help.

Use voice-powered tech

One of the big pluses of AI is that so much can now be done with voice. You can speak so much faster than you can type text, so get into the habit of using your voice to write and issue commands. A very simple but powerful application of this is to use tools like Google Gemini or Microsoft Copilot which integrate with Gmail or Outlook respectively to draft, send, and manage emails via voice commands. This can extend further into task management.

Find user-friendly platforms

There are many AI-powered platforms covering everything from accounting to calendar management to website creation. Look for what others in your network are using. But if it feels like too much, then look for a platform that comes with a service attached, too. Some banks and accounting firms are now using AI tools to help business owners gain better insight into their spending patterns. At UENI, our web design and copywriting experts leverage AI in our proprietary tools to deliver powerful SEO content for pennies you’d pay elsewhere. It’s a smart strategy to work with organizations that know how to get the most out of AI for small businesses.

Do it only if it adds value

AI should either save you time or give you insight into how to do things better. If you are able to usefully save time with a chatbot on your website without compromising your customer’s experience too much or personalize email content, then it is worth carving the time to set these things up or getting help to do it. But always start with what your business uniquely needs to drive the tools you use.

Don’t miss this out: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

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

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Led by former AWS and Oracle AI executives, Protect AI leads in security posture management with the most comprehensive end-to-end platform

Protect AI, the leading artificial intelligence (AI) and machine learning (ML) security company, announced it has closed a $60M Series B round of funding led by Evolution Equity Partners with participation from 01 Advisors, StepStone Group, Samsung, and existing investors Acrew Capital, boldstart ventures, Knollwood Capital, Pelion Ventures, and Salesforce Ventures. To date, the company has raised a total of $108.5M to help organizations protect ML systems and AI applications from unique security vulnerabilities and emerging threats.

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

“AI is being deployed across every industry at an accelerating pace, and organizations have realized they need security guardrails for these systems that are not being covered by incumbent security providers”

Protect AI will use the new financing to drive the next phase of innovation and capabilities for its customers, enhancing its AI security posture management platform. This capital infusion will accelerate the company’s growth by expanding customer success and sales resources, advancing R&D, and strengthening channel programs. Protect AI is positioned to extend its lead in the AI security market, providing unmatched protection for AI applications and systems worldwide.

“In less than 12 months Protect AI has built the leading AI security platform in the market by addressing AI risks end-to-end,” said Richard Seewald, Founder and Managing Partner at Evolution Equity Partners. “By focusing on comprehensive AI security posture management that spans ML models, LLMs and AI supply chain threats, the company is now a trusted partner for national security organizations and Fortune 500 customers, alike.”

Since raising $35M in Series A funding one year ago, Protect AI has solidified its leadership in the AI security market. The Protect AI security posture management (AI-SPM) platform is now used by private and public sector customers to secure traditional ML models, LLMs, ML systems, and AI applications. The platform has expanded from one to five products, becoming the most comprehensive end-to-end AI security solution available.

The company’s huntr AI/ML threat research community has grown to more than 15,000 members who identify and fix vulnerabilities in crucial AI/ML supply chain projects. Protect AI’s five open-source offerings have been downloaded millions of times, and MLSecOps.com continues to lead AI security education and knowledge sharing. With four acquisitions to date — Rebuff, Huntr, Laiyer AI, and SydeLabs — and a 300% year-over-year team growth, Protect AI plans to add 50 more employees by the end of 2024.

“AI is being deployed across every industry at an accelerating pace, and organizations have realized they need security guardrails for these systems that are not being covered by incumbent security providers,” said Ian Swanson, Co-Founder and CEO of Protect AI. “This additional funding provides the resources to extend our technology lead by providing even more unique AI security capabilities for every element of AI-SPM, at every stage of the AI development lifecycle, and serve customers across the globe. Our mission is to lead the AI security category for years to come and help customers build a safer AI-powered world.”

End-to-End AI Protection, from One Platform

AI Security Posture Management, or AI-SPM, is the practice of continuously monitoring, managing, and improving the security of AI systems and their components. It involves identifying vulnerabilities, ensuring compliance with security policies, and implementing measures to protect AI models and data throughout their lifecycle. With AI-SPM, companies can ensure their AI systems operate securely and reliably, minimizing risks of breaches, misuse, and other security threats.

Protect AI’s industry-leading AI-SPM solution offers comprehensive security capabilities such as Guardian, which scans internally built ML models and externally acquired models for threats, and Layer which is a dedicated GenAI security tool designed for LLM security, observability, and governance. Additional services include Radar, providing AI/ML bill of materials with a robust policy engine, and Sightline, which is an AI/ML threat feed derived from the unique supply chain research conducted by the 15K+ Protect AI huntr community.

Also Read: Extreme Networks and Intel Join Forces to Drive AI-Centric Product Innovation

To further assist customers in securing their AI environments and GenAI applications, Protect AI acquired SydeLabs prior to the funding and closing of its Series B investment round. SydeLabs’ product, SydeBox, is an automated red teaming tool for GenAI systems that helps customers identify vulnerabilities, ensure safer model selection, and continuously improve the security of their LLM based applications.

“As companies are evaluating LLMs to power AI applications, it is essential to red team across multiple categories of risk from jailbreaks to prompt extraction and susceptibility to prompt injection attacks. SydeBox is the most advanced AI red teaming suite, and we are excited to add this solution to the Protect AI platform,” said SydeLabs Co Founder Ankita Kumari.

Channel partners, integrators, and resellers will also benefit significantly from the Protect AI platform and continued product advancements. By leveraging Protect AI’s comprehensive AI-SPM platform, partners can ensure their clients’ AI environments are secure, compliant, and resilient against threats.

World Wide Technology (WWT), a global technology solutions provider with $20B in annual revenue, is at the forefront of the secure AI Digital Revolution. By integrating strategy, execution, and partnerships, WWT accelerates secure AI transformations for large public and private organizations worldwide.

According to Todd Hathaway, Global Practice Manager for AI Security Solutions at WWT: “Our partnership with Protect AI perfectly aligns with our Global Cyber mission to deliver digital security excellence. Protect AI’s comprehensive AI security platform enhances our efforts to provide cutting-edge, secure AI and generative AI outcomes. With offerings for first and third party model security, LLM Security, AI-SPM, an AI/ML vulnerability database, and automated red teaming for GenAI systems, Protect AI stands unmatched in its breadth of security coverage for AI/ML systems it makes available to our customers.”

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

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

The post Protect AI Raises $60M in Series B Financing to Secure Artificial Intelligence and Machine Learning from Unique Security Risks appeared first on AiThority.

HatchWorks AI announces the launch of GenIQ, an AI-driven process intelligence platform transforming software development. Utilizing Bloomfilter and HatchWorks’ Generative-Driven Development, GenIQ identifies inefficiencies throughout the software development lifecycle (SDLC) and pinpoints where best to apply AI to maximize its effectiveness.

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

“GenIQ is a transformational approach to software development,” said Brandon Powell, CEO at HatchWorks AI. “Rooted in our pioneering Generative-Driven Development™ methodology, GenIQ empowers technology leaders to identify inefficiencies and leverage generative AI as a competitive advantage, ensuring projects are completed on time and within budget, setting new benchmarks for innovation.”

Also Read: Extreme Networks and Intel Join Forces to Drive AI-Centric Product Innovation

Generative AI promises to enhance development productivity, however, measuring ROI is difficult. 56% of enterprise leaders believe ROI is positive, but aren’t precisely measuring. GenIQ not only helps you identify and address gaps in your SDLC with AI-driven process intelligence but also measure the ROI of AI.

GenIQ offers unmatched transparency and predictability, integrating with systems like Jira, GitHub, Figma, Asana, and Azure, enabling leaders to:

• Observe, measure, and improve process productivity.
• Identify optimal areas for Gen AI application.
• Measure the ROI of Gen AI initiatives.
• Make informed, ROI-driven decisions with advanced predictability on project timelines, costs, and outputs.

“We’re thrilled to join forces with HatchWorks AI to roll out GenIQ,” said Erik Severinghaus, Co-Founder & Co-CEO of Bloomfilter. “Having witnessed firsthand the transformative power of process mining the SDLC, we know it can significantly boost team success. Leveraging AI to enhance productivity and make software development more observable, predictable, and efficient not only saves time and reduces waste but also ensures successful software delivery.”

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

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

The post HatchWorks AI Unveils GenIQ: Revolutionizing Software Development with AI-Driven Process Intelligence appeared first on AiThority.