IBM’s current focal point in AI research and development lies in applying it to technology operations. As Ball explained, “As people try to build applications out in the world, it’s an increasingly complex situation. There are so many tools, there are so many environments that go into building and maintaining an application over time that a lot of teams are just drowned under all of the data that’s involved.”

To tackle this challenge, the company has announced IBM Concert, which will harness AI to make sense of the vast amount of data involved in application development and maintenance. “It’s using AI to figure out actually how your application works, and then provides recommendations about how to make it better,” Ball said.

Upcoming AI opportunities

According to Ball, a current opportunity is organising the unstructured data that feeds into AI models. “There can be a gap between the unstructured amoeba of data and then what you want in AI, which is sorted, ready to go,” he acknowledged. However, IBM is actively working to bridge this gap, with IBM Concert set to evolve and incorporate tools to organise data into a format more digestible for AI engines.

Explainability is another critical aspect of AI that IBM is addressing with IBM Concert. Ball emphasised the importance of not blindly accepting AI recommendations, stating, “We’re actually building in a function that you can question the recommendation so that you can question what the AI comes up with, and sort of dig a little bit deeper into how it came to that conclusion.”

Powering the future of work

Beyond IBM Concert, IBM offers a suite of AI technologies and tools, such as watsonx and AI governance solutions. As Ball explained, IBM aims to provide a “use-case-neutral” approach, allowing customers to leverage IBM’s AI capabilities for their specific needs.

One area where IBM has seen early success with IBM Concert is in addressing the data overload faced by many organisations. Ball shared that design partners have been “amazed at what we’re able to do, the insights that we’re able to show, even at a very basic level.” As IBM Concert’s capabilities continue to evolve, IBM expects to deliver even more profound insights and conclusions, ultimately improving application performance, security, and overall management.

For organisations considering adopting AI for the first time, Ball’s advice is clear: “Be deliberate about what you want to do with it. Don’t come in just thinking that the technology itself is the goal, but have a real use case in mind, a real goal in mind that you want AI to accomplish.”

At the upcoming Intelligent Automation Conference, where IBM is a key sponsor, the company plans to showcase IBM Concert and its potential to transform technology operations through the power of AI.

As the interview concluded, Ball expressed excitement about the possibilities of IBM Concert, stating, “We’re really excited about this, and we think our customers are going to be really excited about it too.”

You can watch our full interview with Ben Ball below:

Gain further insights from Ben Ball as he shares his expertise in his day one presentation titled ‘Leveraging Gen AI to proactively mitigate security vulnerabilities in your applications’ at the Intelligent Automation Conference.

Want to learn more about intelligent automation from industry leaders? Check out the Intelligent Automation Conference taking place in California, London, and Amsterdam. The comprehensive event is co-located with other leading events including AI & Big Data Expo, IoT Tech Expo, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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This collaboration combines Tech Mahindra’s range of AI offerings, TechM amplifAI0->∞, and IBM’s watsonx AI and data platform with AI Assistants.

Customers can now combine IBM watsonx’s capabilities with Tech Mahindra’s AI consulting and engineering skills to access a variety of new generative AI services, frameworks, and solution architectures. This enables the development of AI apps in which organisations can use their trusted data to automate processes. It also provides a basis for businesses to create trustworthy AI models, promotes explainability to help manage risk and bias, and enables scalable AI adoption across hybrid cloud and on-premises environments.

According to Kunal Purohit, Tech Mahindra’s chief digital services officer, organisations focus on responsible AI practices, and incorporating generative AI technologies to revitalise enterprises. 

“Our work with IBM can help advance digital transformation for organisations, adoption of GenAI, modernisation, and ultimately foster business growth for our global customers,” Purohit added.

To further enhance business capabilities in AI, Tech Mahindra has established a virtual watsonx Centre of Excellence (CoE), which is already operational. This CoE functions as a co-innovation centre, with a dedicated team tasked with maximising synergies between the two companies and producing unique offerings and solutions based on their combined capabilities.

The collaborative offerings and solutions developed through this partnership could help enterprises achieve their goals of constructing machine learning models using open-source frameworks while also enabling them to scale and accelerate the impact of generative AI. These AI-driven solutions have the potential to aid organisations enhance efficiency and productivity responsibly. 

Kate Woolley, GM of IBM Ecosystem, emphasised the collaboration’s potential, adding that generative AI may serve as a catalyst for innovation, unlocking new market opportunities when built on a foundation of explainability, transparency, and trust. 

Woolley said: “Our work with Tech Mahindra is expected to expand the reach of watsonx, allowing even more customers to build trustworthy AI as we seek to combine our technology and expertise to support enterprise use cases such as code modernisation, digital labour, and customer service.”

This collaboration aligns with Tech Mahindra’s continuous endeavour to transform enterprises with advanced AI-led offerings and solutions, including their recent additions like Vision amplifAIer, Ops amplifAIer, Email amplifAIer, Enterprise Knowledge Search offering, Evangelize Pair Programming, and Generative AI Studio.

It is worth mentioning that the two companies have previously collaborated. Earlier this year, Tech Mahindra announced the opening of a Synergy Lounge in conjunction with IBM on the company’s Singapore campus. This Lounge seeks to accelerate digital adoption for APAC organisations. It aids in operationalising and leveraging next-generation technologies such as AI, intelligent automation, hybrid cloud, 5G, edge computing, and cybersecurity.

Beyond Tech Mahindra, IBM watsonx has been used in other collaborations to speed up the deployment of generative AI. Also happened early this year, the GSMA and IBM announced a new partnership to support the use and capabilities of generative AI in the telecom industry by launching GSMA Advance’s AI Training program and the GSMA Foundry Generative AI program.

In addition, there is a digital version of the program that covers both the commercial strategy and technology fundamentals of generative AI. This initiative uses IBM watsonx to provide hands-on training for architects and developers seeking in-depth practical generative AI knowledge.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The extended partnership between Wipro and IBM combines the former’s extensive industry expertise with IBM’s leading AI innovations. The collaboration seeks to develop joint solutions that facilitate the implementation of robust, reliable, and enterprise-ready AI solutions.

The Wipro Enterprise AI-Ready Platform harnesses various components of the IBM watsonx suite, including watsonx.ai, watsonx.data, and watsonx.governance, alongside AI assistants. It offers clients a comprehensive suite of tools, large language models (LLMs), streamlined processes, and robust governance mechanisms, laying a solid foundation for the development of future industry-specific analytic solutions.

Jo Debecker, Managing Partner & Global Head of Wipro FullStride Cloud, said: “This expanded partnership with IBM combines our deep contextual cloud, AI, and industry expertise with IBM’s leading AI innovation capabilities.”

A key aspect of this collaboration is the establishment of the IBM TechHub@Wipro, a centralised tech hub aimed at supporting joint client pursuits. This initiative will bring together subject matter experts, engineers, assets, and processes to drive and support AI initiatives.

Kate Woolley, General Manager of IBM Ecosystem, commented: “We’re pleased to reach this new milestone in our 20-year partnership to support clients through the combination of Wipro’s and IBM’s joint expertise and technology, including watsonx.”

The Wipro Enterprise AI-Ready Platform offers infrastructure and core software for AI and generative AI workloads, enhancing automation, dynamic resource management, and operational efficiency in the enterprise. Moreover, it caters to specialised industry use cases, such as banking, retail, health, energy, and manufacturing, offering tailored solutions for customer support, marketing, feedback analysis, and more.

Nagendra Bandaru, Managing Partner and President of Wipro Enterprise Futuring, highlighted the flexibility of the platform, stating: “Wipro’s Enterprise AI-Ready Platform will allow clients to easily integrate and standardise multiple data sources augmenting AI- and GenAI-enabled transformation across business functions.”

In addition to facilitating AI governance through the AI lifecycle, the platform prioritises responsible AI practices, ensuring transparency, data protection, and compliance with relevant laws and regulations.

As part of this collaboration, Wipro associates will undergo training in IBM hybrid cloud, AI, and data analytics technologies, further enhancing their capabilities in developing joint solutions.

(Photo by Carson Masterson on Unsplash)

See also: Reddit is reportedly selling data for AI training

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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This breakthrough, published in a recent paper in Nature Electronics, signifies a significant stride towards achieving high-performance AI computing while substantially conserving energy.

The traditional approach of executing deep neural networks on conventional digital computing architectures poses limitations in terms of performance and energy efficiency. These digital systems entail constant data transfer between memory and processing units, slowing down computations and reducing energy optimisation.

To tackle these challenges, IBM Research has harnessed the principles of analog AI, which emulates the way neural networks function in biological brains. This approach involves storing synaptic weights using nanoscale resistive memory devices, specifically Phase-change memory (PCM).

PCM devices alter their conductance through electrical pulses, enabling a continuum of values for synaptic weights. This analog method mitigates the need for excessive data transfer, as computations are executed directly in the memory—resulting in enhanced efficiency.

The newly introduced chip is a cutting-edge analog AI solution composed of 64 analog in-memory compute cores.

Each core integrates a crossbar array of synaptic unit cells alongside compact analog-to-digital converters, seamlessly transitioning between analog and digital domains. Furthermore, digital processing units within each core manage nonlinear neuronal activation functions and scaling operations. The chip also boasts a global digital processing unit and digital communication pathways for interconnectivity.

The research team demonstrated the chip’s prowess by achieving an accuracy of 92.81 percent on the CIFAR-10 image dataset—an unprecedented level of precision for analog AI chips.

The throughput per area, measured in Giga-operations per second (GOPS) by area, underscored its superior compute efficiency compared to previous in-memory computing chips. This innovative chip’s energy-efficient design coupled with its enhanced performance makes it a milestone achievement in the field of AI hardware.

The analog AI chip’s unique architecture and impressive capabilities lay the foundation for a future where energy-efficient AI computation is accessible across a diverse range of applications.

IBM Research’s breakthrough marks a pivotal moment that will help to catalyse advancements in AI-powered technologies for years to come.

(Image Credit: IBM Research)

See also: Azure and NVIDIA deliver next-gen GPU acceleration for AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

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The geospatial model, built from NASA’s satellite data, will be the largest of its kind on Hugging Face and marks the first-ever open-source AI foundation model developed in collaboration with NASA.

Jeff Boudier, head of product and growth at Hugging Face, highlighted the importance of information sharing and collaboration in driving progress in AI. Open-source AI and the release of models and datasets are fundamental in ensuring AI benefits as many people as possible.

Climate science faces constant challenges due to rapidly changing environmental conditions, requiring access to the latest data. Despite the abundance of data, scientists and researchers struggle to analyse the vast datasets effectively. NASA estimates that by 2024, there will be 250,000 terabytes of data from new missions.

To address this issue, IBM embarked on a Space Act Agreement with NASA earlier this year—aiming to build an AI foundation model for geospatial data.

By making this geospatial foundation model openly available on Hugging Face, both companies aim to promote collaboration and accelerate progress in climate and Earth science.

Sriram Raghavan, VP at IBM Research AI, commented:

“The essential role of open-source technologies to accelerate critical areas of discovery such as climate change has never been clearer.

By combining IBM’s foundation model efforts aimed at creating flexible, reusable AI systems with NASA’s repository of Earth-satellite data, and making it available on the leading open-source AI platform, Hugging Face, we can leverage the power of collaboration to implement faster and more impactful solutions that will improve our planet.”

The geospatial model, jointly trained by IBM and NASA on Harmonized Landsat Sentinel-2 satellite data (HLS) over one year across the continental United States, has shown promising results. It demonstrated a 15 percent improvement over state-of-the-art techniques using only half the labelled data.

With further fine-tuning, the model can be adapted for various tasks such as deforestation tracking, crop yield prediction, and greenhouse gas detection.

IBM’s collaboration with NASA in building the AI model aligns with NASA’s decade-long Open-Source Science Initiative, promoting a more accessible and inclusive scientific community. NASA, along with other federal agencies, has designated 2023 as the Year of Open Science, celebrating the benefits of sharing data, information, and knowledge openly.

Kevin Murphy, Chief Science Data Officer at NASA, said:

“We believe that foundation models have the potential to change the way observational data is analysed and help us to better understand our planet.

By open-sourcing such models and making them available to the world, we hope to multiply their impact.”

The geospatial model leverages IBM’s foundation model technology and is part of IBM’s broader initiative to create and train AI models with transferable capabilities across different tasks.

In June, IBM introduced watsonx, an AI and data platform designed to scale and accelerate the impact of advanced AI with trusted data. A commercial version of the geospatial model, integrated into IBM watsonx, will be available through the IBM Environmental Intelligence Suite (EIS) later this year.

By leveraging the power of open-source technologies, this latest collaboration aims to address climate challenges effectively and contribute to a more sustainable future for our planet.

(Photo by Markus Spiske on Unsplash)

See also: Jay Migliaccio, IBM Watson: On leveraging AI to improve productivity

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

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The features include generative AI commentary and AI draw analysis, aimed at enhancing the engagement and insight for tennis enthusiasts.

Usama Al-Qassab, Marketing & Commercial Director at The All England Club, said:

“We are constantly innovating with our partners at IBM to provide Wimbledon fans, wherever they are in the world, with an insightful and engaging digital experience of The Championships.

This year, we’re introducing new features for our digital platforms that use the latest AI technology from IBM to help fans gain even more insight into the singles draw and access commentary on a wider variety of matches through our match highlights videos.”

AI commentary

The AI commentary feature, developed in collaboration with IBM iX, offers fans watching match highlights videos the option to receive audio commentary and captions of key moments.

The tool aims to provide a more insightful experience when catching up on matches through the Wimbledon App and wimbledon.com. This introduction marks a step towards making commentary available for matches outside of Wimbledon’s Show Courts, which already have live human commentary.

Experts from IBM iX and The All England Club worked together to train the AI using foundation models from IBM’s enterprise AI and data platform, watsonx.

The generative AI employed in this feature produces narration with diverse sentence structures and vocabulary, enhancing the informative and engaging nature of the clips.

AI draw analysis

Another innovative feature introduced this year is the AI draw analysis, a first of its kind in tennis.

Leveraging AI, this feature provides a statistic to determine the favorability of the path to the final for each player in the singles draw. Factors such as match-ups against potential future opponents and the player’s position in the draw compared to competitors contribute to the player’s draw favorability rating.

This unique insight enables tennis fans to uncover anomalies and potential surprises in the singles draw that may not be evident solely by considering the players’ rankings. The feature aims to inspire more debate and engagement within the fan community.

Jonathan Adashek, SVP of Marketing and Communications for IBM, commented:

“IBM is bringing new layers of insight and engagement to the 2023 Championships through the use of innovative new tools, powered by foundation models and generative AI from watsonx.

We’ve seen first-hand how these technologies have the power to help major sporting events like Wimbledon to grow their audiences through outstanding digital experiences. 

The AI and data platform that IBM is using to create unique fan experiences for Wimbledon is the same technology that we’re using to drive business transformation with clients across all sectors and industries.”

These new features complement the existing suite of AI-powered tools available on the Wimbledon App and wimbledon.com. The suite includes the IBM Power Index Leaderboard, IBM Match Insights, and Personalized Highlights Reels and Recommendations.

These digital features leverage IBM’s Watson AI technology to analyse over 100,000 data points from every shot played throughout the tournament, providing fans with a deeper understanding of the players, their opponents, and likely outcomes. The constant updates and tailored insights keep fans engaged throughout the tournament.

Wimbledon will take place from July 3 to July 16, 2023. Fans can witness these AI-powered features in action by visiting wimbledon.com or downloading the Wimbledon App from the App Store or Play Store.

(Photo by Ariv Gupta on Unsplash)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

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In 1997, IBM’s Deep Blue supercomputer was used to beat World Chess Champion Garry Kasparov. At the time, all too familiar headlines suggested that computers would soon replace humans. Over two decades later, AI has proven to be an assistive tool that benefits us every day.

IBM Watson’s first commercial application was announced a little over a decade ago in February 2013 for utilisation management decisions in lung cancer treatment. In the years since, we’ve seen it used to deliver game-changing advancements in healthcare, weather forecasting, education, science, and much more.

AI News caught up with Jay Migliaccio, Senior Product Manager for Watson Orchestrate, to learn how IBM is now using its vast experience to help businesses with their digital transformations.

AI News: So, Jay, can you tell me how IBM is helping businesses to improve the productivity of their workforces?

Jay Migliaccio: Yes, Ryan. Thanks so much for the invite and for asking me here.

IBM is expanding its suite of offerings in the area of digital labour. Digital labour leverages AI and automation to help workers become more productive. And, much like human labour, digital labour performs work on business systems through “skills”.

Digital labour skills enable digital labour to interact with business applications, much like you and I would interact with a system of record or system of engagement. We can do this now through digital labour. And, what’s new and unique, is that digital labour leverages the human-centric interaction style.

So, we’ve introduced natural language and we’ve also introduced intelligent orchestration to be able to execute not just single skills, but actually multiple skills to be able to achieve higher-level tasks.

AN: Generative AI is a hot topic in the market at the moment. Do you see that being used practically in the workplace and what risks should businesses be aware of?

JM: Yeah, great question. I actually do use it myself in the workplace, I occasionally have to develop software tools and simple scripts and I have had it generate a number of scripts for me successfully. So I’m impressed not just with its ability to generate verbal and written content, but also code content. I for sure believe it will become increasingly useful in the workplace.

Current generative AI platforms have been trained on the internet, so remember your results may vary. I know anytime I Google or search for things on the internet I take the results with a grain of salt.

I believe that enterprises, as they go to look and adopt generative AI systems, they’ll lean more towards AI that they can trust. Therefore, we need to work on being able to create that trust element in generative AI solutions.

AN: What is the value of Watson Orchestrate for developers?

JM: When we talk about developers, I’m talking about automation developers. And that’s by and large developers that are integrating apps and business apps and business systems to work together.

For the most part, those developers have been integrating business systems to business systems. Now what we can do with Watson Orchestrate is we can introduce the human into the loop.

These automation developers now have a platform where they can build and integrate their automation workflows and they can bring a human experience into these automation workflows for everyday human workers.

Watson Orchestrate provides a platform for creating human-centric workflow automation, designed to interact with humans in our native communication style which is spoken or written word.

AN: How does Watson Orchestrate learn from user interactions?

JM: There are a couple of ways Watson Orchestrate is monitoring the behaviour of humans and learning from them. 

Perhaps most important is its ability to interpret the natural language through which humans are communicating. Today it’s the written word, but in the future spoken word. Watson Orchestrate can not just do a pattern match based on existing known sentences, but it can actually understand the intent of those utterances. 

Additionally, it can extract entities from those utterances. So, when you use proper nouns in a sentence, it can understand that’s an entity that it would use as part of an automation. It can match the intent of the utterance to existing skills that it has and can react accordingly. It can understand the intent of the utterance and then take action on those skills. Increasingly, it can sequence multiple skills together.

Also, we are working on systems for empowering Watson Orchestrate to monitor the user’s behaviour. And, just like any modern SaaS application today that has recommendations based on your behaviour, we’re working on recommendation engines to recommend to employees how they can use Watson Orchestrate to be more productive in the future.

AN: Talking about AI more generally, what new ways of working are today’s advancements enabling?

JM: As I just alluded to, we’re increasingly empowering systems to understand human natural language to a much more complex and sophisticated extent. Natural language interpretation has grown way beyond the basic pre-programmed bot experience.

I’m sure everybody has had an experience on a website where there’s a bot responding to your basic questions. What we’re trying to do is make that bot much more intelligent. The current generation of digital labour can understand your intent, extract entities from your utterances, and, most importantly, take action on your behalf.

AN: On the flip side, what are some of the main dangers of automation tools and how do we overcome those?

JM: I’m not sure if this is a specific category of danger, but I suppose I would put it under the law of unintended consequences. Anytime you work with technology, there can be outcomes that you don’t expect.

For example, if we think about the automobile as an automation tool for moving humans around – the intent, of course, was to move a human from A to B faster, and maybe more reliably. But the net result is occasionally we have accidents.

Much like the way we build transportation systems to constrain and reduce the potential for accidents, we have to do the same thing in our business systems with digital labour. Certainly, we’re going to want to start small and just do very selective, very specific tasks that are well-curated and well-defined.

We’ll need to create guardrails that guard against unintended and unexpected behaviour. One of the ways we’re doing this in Watson Orchestrate is to empower the digital labour to act on the user’s behalf and therefore leverage the user’s credentials when interacting with business systems.

As an employee, I’m given certain access to a business system based on my role. Therefore, we know when the digital employee performs actions on my behalf it also has those existing restrictions and permissions for those business systems.

Another option is monitoring behaviour and monitoring for unintended consequences. And, lastly, integrating governance and creating policies that permit or restrict the behaviour of digital employees.

AN: Are you a believer in the metaverse? If so, how much work do you think we will be doing in it?

JM: Yeah, great question. Metaverse for me is a very loose term. Here we are digitally speaking – we’ve never met before, you could be an avatar for all I know. So, in that sense, I’m a believer in the metaverse.

For me, like most innovative technologies, it will start on the fringe and work its way into the mainstream. You can look at entertainment and gaming and see very metaverse-like experiences being used there.

I’ve seen examples of the metaverse being used for deep meditative experiences. If you want to go into deep meditation, you can put on your virtual headsets and enter a metaverse world that is very different from the physical world we live in.

And I also can see the metaverse being initially used for education purposes. I think it’s a great way – a sort of low-risk way – to introduce people to new environments and new ideas at scale.

I don’t think we’re gonna go to the metaverse to go to work. I don’t see that as something coming in the near term.

AN: I can only promise I’m not an avatar for the time being. I might bump into you at Digital Transformation Week, next week. You’ll obviously be in attendance, what will you be sharing with the audience at the event?

JM: Yeah, Digital Transformation Week I will be talking about our view on the digital labour market and some of our solutions. I will also be sharing some of the stories about the early adopters of IBM’s digital labour solutions.

You can watch our full interview with Jay below:

Looking to revamp your intelligent automation strategy? Learn more about the Intelligent Automation Event & Conference, to discover the latest insights surrounding unbiased algorithyms, future trends, RPA, Cognitive Automation and more!

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The Mayflower – named after the ship which carried Pilgrims from Plymouth, UK to Massachusetts, US in 1620 – is a 50-foot crewless vessel that relies on AI and edge computing to navigate the often harsh and unpredictable oceans.

IBM’s Mayflower has been attempting to autonomously complete the voyage that its predecessor did over 400 years ago but has been beset by various problems.

The initial launch was planned for June 2021 but a number of technical glitches forced the vessel to return to Plymouth.

Back in April 2022, the Mayflower set off again. This time, an issue with the generator forced the boat to divert to the Azores Islands in Portugal.

The Mayflower was patched up and pressed on until late May when a problem developed with the charging circuit for the generator’s starter batteries. This time, a course for Halifax, Nova Scotia was charted.

After more than five weeks since it departed Plymouth, the modern Mayflower is now docked in Halifax. While it’s yet to reach its final destination, the Mayflower has successfully crossed the Atlantic (hiccups aside.)

While mechanically the ship leaves a lot to be desired, IBM says the autonomous systems have worked flawlessly—including the AI captain developed by MarineAI.

It’s beyond current AI systems to instruct and control robotics to carry out mechanical repairs for any number of potential failures. However, the fact that Mayflower’s onboard autonomous systems have been able to successfully navigate the ocean and report back mechanical issues is an incredible achievement.

“It will be entirely responsible for its own navigation decisions as it progresses so it has very sophisticated software on it—AIs that we use to recognise the various obstacles and objects in the water, whether that’s other ships, boats, debris, land obstacles, or even marine life,” Robert High, VP and CTO of Edge Computing at IBM, told Edge Computing News in an interview.

IBM designed Mayflower 2.0 with marine research nonprofit Promare. The ship uses a wind/solar hybrid propulsion system and features a range of sensors for scientific research on its journey including acoustic, nutrient, temperature, and water and air samplers.

You can find out more about the Mayflower and view live data and webcams from the ship here.

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Watson Discovery is an AI-powered intelligent search and text-analytics platform that can retrieve critical information buried in enterprise data.

In one case study, Woodside Energy had no way to retrieve the 30 years’ worth of valuable engineering and drilling knowledge that was buried in unstructured documentation. Using the existing NLP capabilities of Watson Discovery, the firm reportedly cut research time by more than 75 percent.

Among the new enhancements planned for Watson Discovery are:

• Pre-trained document structure understanding: Watson Discovery’s Smart Document Understanding feature now includes a new pre-trained model designed to automatically understand the visual structure and layout of a document without additional training from a developer or data scientist.
• Automatic text pattern detection: A new advanced pattern creation feature is available in beta that helps users to quickly identify business-specific text patterns within their documents. It can start learning the underlying text patterns from as little as two examples and then refines the pattern based on user feedback.
• Advanced NLP customisation capabilities: With a new custom entity extractor feature, IBM is simplifying the process of training NLP models to identify highly-customised, business-specific words by reducing the data prep effort, simplifying labeling with active learning and bulk annotation capabilities, and enabling simple model deployment to accelerate training time.

“The stream of innovation coming to IBM Watson from IBM Research is why global businesses in the fields of financial services, insurance, and legal services turn to IBM to help detect emerging business trends, gain operational efficiency and empower their workers to uncover new insights,” said Daniel Hernandez, General Manager of Data and AI, IBM.

“The pipeline of natural language processing innovations we’re adding to Watson Discovery can continue to provide businesses with the capabilities to more easily extract the signal from the noise and better serve their customers and employees.”

(Image Credit: IBM)

Looking to revamp your digital transformation strategy? Learn more about the Digital Transformation Week event taking place in Amsterdam on 23-24 November 2021 and discover key strategies for making your digital efforts a success.

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The company’s roster of accelerated computing kits now exceeds 150 and supports the almost three million developers in NVIDIA’s Developer Program.

Four of the major new SDKs are:

• ReOpt – Automatically optimises logistical processes using advanced, parallel algorithms. This includes vehicle routes, warehouse selection, and fleet mix. The dynamic rerouting capabilities – shown in an on-stage demo – can reduce travel time, save fuel costs, and minimise idle periods.
• cuNumeric – Implements the popular NumPy application programming interface and enables scaling to multi-GPU and multi-node systems with zero code changes.
• cuQuantum – Designed for quantum computing, it enables large quantum circuits to be simulated faster. This enables quantum researchers to simulate areas such as near-term variational quantum algorithms for molecules, error correction algorithms to identify fault tolerance, and accelerate popular quantum simulators from Atos, Google, and IBM.
• CUDA-X accelerated DGL container – Helps developers and data scientists working on graph neural networks to quickly set up a working environment. The container makes it easy to work in an integrated, GPU-accelerated GNN environment combining DGL and Pytorch.

Some existing AI-related SDKs that have received notable updates are:

• Deepstream 6.0 – introduces a new graph composer that makes computer vision accessible with a visual drag-and-drop interface.
• Triton 2.15, TensorRT 8.2 and cuDNN 8.4 – assists with the development of deep neural networks by providing new optimisations for large language models and inference acceleration for gradient-boosted decision trees and random forests.
• Merlin 0.8 – boosts recommendation systems with its new capabilities for predicting a user’s next action with little or no user data and support for models larger than GPU memory.

Accelerating quantum research

Nvidia has established a partnership with Google, IBM, and a number of small companies, national labs, and university research groups to accelerate quantum research.

“It takes a village to nurture an emerging technology, so Nvidia is collaborating with Google Quantum AI, IBM, and others to take quantum computing to the next level,” explained the company in a blog post.

The first library from the aforementioned new cuQuantum SDK is Nvidia’s initial contribution to the partnership. The library is called cuStateVec and is an accelerator for the state vector simulation method which tracks the full state of the system in memory and can scale to tens of qubits.

cuStateVec has been integrated into Google Quantum AI’s state vector simulator qsim and can be used through the open-source framework Cirq.

“Quantum computing promises to solve tough challenges in computing that are beyond the reach of traditional systems,” commented Catherine Vollgraff Heidweiller at Google Quantum AI.

“This high-performance simulation stack will accelerate the work of researchers around the world who are developing algorithms and applications for quantum computers.”

In December, cuStateVec will also be integrated with Qiskit Aer—a high-performance simulator framework for quantum circuits from IBM.

Among the national labs using cuQuantum to accelerate their research are Oak Ridge, Argonne, Lawrence Berkeley National Laboratory, and Pacific Northwest National Laboratory. University research groups include those at Caltech, Oxford, and MIT.

Nvidia is helping developers to get started by creating a ‘DGX quantum appliance’ that puts its simulation software in a container optimised for its DGX A100 systems. The software will be available early next year via the company’s NGC Catalog.

(Image Credit: Nvidia)

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