From addressing the spectrum of use cases to navigating digital transformation, Shakir shed light on the challenges, ethical considerations, and the promising future of this groundbreaking technology.

Wide spectrum of use cases

Shakir highlighted the wide array of GenAI applications, ranging from productivity enhancements and research support to high-stakes areas such as strategic data mining and knowledge bots. She emphasised the transformational power of AI in understanding customer data, moving beyond simple sentiment analysis to providing actionable insights, thus elevating customer engagement strategies.

“GenAI now can take your customer insights to another level. It doesn’t just tell you whether something’s a positive or negative sentiment like old AI would do, it now says it’s positive or negative. It’s negative because X, Y, Z, and here’s the root cause for X, Y, Z,” explains Shakir.

Powering digital transformation

Gate One adopts an adaptive strategy approach, abandoning traditional five-year strategies for more agile, adaptable frameworks.

“We have a framework – our 5P model – where it’s: identify your people, identify the problem statement that you’re trying to solve for, appoint some partnerships, think about what’s the right capability mix that you have, think about the pathway through which you’re going to deliver, be use case or risk-led, and then proof of concept,” says Shakir.

By solving specific challenges and aligning strategies with business objectives, Gate One aims to drive meaningful digital transformation for its clients.

Assessing client readiness

Shakir discussed Gate One’s diagnostic tools, which blend technology maturity and operating model innovation questions to assess a client’s readiness to adopt GenAI successfully.

“We have a proprietary tool that we’ve built, a diagnostic tool where we look at blending tech maturity capability type questions with operating model innovation questions,” explains Shakir.

By categorising clients as “vanguard” or “safe” players, Gate One tailors their approach to meet individual readiness levels—ensuring a seamless integration of GenAI into the client’s operations.

Key challenges and ethical considerations

Shakir acknowledged the challenges associated with GenAI, especially concerning the quality of model outputs. She stressed the importance of addressing biases, amplifications, and ethical concerns, calling for a more meaningful and sustainable implementation of AI.

“Poor quality data or poorly trained models can create biases, racism, sexism… those are the things that worry me about the technology,” says Shakir.

Gate One is actively working on refining models and data inputs to mitigate such problems.

The future of GenAI

Looking ahead, Shakir predicted a demand for more ethical AI practices from consumers and increased pressure on developers to create representative and unbiased models.

Shakir also envisioned a shift in work dynamics where AI liberates humans from mundane tasks to allow them to focus on solving significant global challenges, particularly in the realm of sustainability.

Later this month, Gate One will be attending and sponsoring this year’s AI & Big Data Expo Global. During the event, Gate One aims to share its ethos of meaningful AI and emphasise ethical and sustainable approaches.

Gate One will also be sharing with attendees GenAI’s impact on marketing and experience design, offering valuable insights into the changing landscape of customer interactions and brand experiences.

As businesses navigate the evolving landscape of GenAI, Gate One stands at the forefront, advocating for responsible, ethical, and sustainable practices and ensuring a brighter, more impactful future for businesses and society.

Umbar Shakir and the Gate One team will be sharing their invaluable insights at this year’s AI & Big Data Expo Global. Find out more about Umbar Shakir’s day one keynote presentation here.

The post Umbar Shakir, Gate One: Unlocking the power of generative AI ethically appeared first on AI News.

Glean, a firm established by Google search engineers and other industry veterans, possesses considerable expertise in this area.

AI News caught up with Arvind Jain, CEO and Founder of Glean, to hear more about how the company is using AI to surface workplace knowledge and supercharge productivity.

AI News: Can you tell us about Glean and its goals?

Arvind Jain: Glean is solving perhaps the most urgent problem in today’s workplace: helping people find and access the information they need to do their best work. 

Over the past four years, we’ve built a search platform that leverages the latest advancements in machine learning and retrains deep learning language models on each company’s specific knowledge base. In this way, we develop a deep understanding of context, language, behaviour, and relationships with others that are uniquely tuned to your workplace and adheres to your data governance policies: a trusted knowledge model.  

Our trusted knowledge model enables us to give users the most relevant and personalised answers to their queries and empowers them to stay connected not only to company knowledge but also to one another.

Our mission is to bring everyone the knowledge they need to make a difference in the world.

AN: Has more remote working post-pandemic increased the need for knowledge-sharing solutions like Glean?

AJ: Absolutely, and this is compounded by the explosion of SaaS applications across the enterprise. Company knowledge has become fragmented and siloed, which presents huge challenges for companies who care about employee experience — who want to ensure that their employees can find answers to their questions, access the information they need, and feel connected.

A Forrester found that the top reason employees feel disengaged from work is that data and/or information is hard to find. Now more than ever it’s absolutely critical to ensure that you invest in good search and knowledge management tools.

AN: How do you ensure that potentially sensitive company data that not all team members should maybe have access to is kept safe?

AJ: Glean was built from the ground up to prioritize security and governance — we connect to all your enterprise knowledge and enforce the existing permissions of your data sources. This has been foundational to our success.

Glean’s governance engine ensures that users only see the information they are allowed to, based on their existing access permissions in the source systems that Glean searches. 

AN: After a reasonable time of integrating Glean, what is the success rate of receiving an answer that someone needs?

AJ: The average Glean user makes 20 searches/day and saves 2-3 hours/week — customer time savings and productivity gains are key success metrics for us.

AN: How does Glean differentiate from its competitors?

AJ: Over the past four years, we’ve built a search platform that leverages the latest advancements in machine learning and retrains deep learning language models on each company’s specific knowledge base.

In this way, we develop a deep understanding of context, lexicon, behaviour, and relationships with others that are uniquely tuned to your workplace and adheres to your data governance policies: a trusted knowledge model. Our trusted knowledge model enables us to provide users with the most relevant and personalised answers to their queries. 

I was also frustrated by how long it took other tools to get up and running. It was very important to me that our search solution should be fully customisable, but also should only need minimal operational overhead to set up – no third-party engagements or professional services.

AN: Amid global economic uncertainties, have you noticed an uptick in interest from enterprises seeking ways to lower their costs and improve operational efficiencies?

AJ: Yes, 100 percent! The economy has simultaneously seen a drop in productivity and in employee engagement, and many businesses are looking to improve efficiency and productivity. 

There are many studies, including one from McKinsey, that have found that almost 20 percent of the work week is spent looking for internal information or colleagues who can help – this is why it’s so vital to empower employees with good tools to connect to company knowledge and resources. It’s a critical investment right now.

AN: What can we expect from Glean over the coming year?

AJ: Generative AI has the potential to supercharge knowledge workers, and everyone wants to figure out how to bring it into the workplace, but GPT-4 and similar Generative AI models are simply not ready for the enterprise.  They need to be grounded in the right search technology. 

Our goal is to deliver a product that’s as useful for the enterprise as ChatGPT is for the web.

With that goal in mind, we’re going to expand the use of generative AI in our offerings and deliver new features, grounded in our trusted knowledge model, that will augment people’s potential at work.

The conversational interface is just one piece of that. Our mission is to bring people the knowledge they need to make a difference in the world. In the background, we’re also continually working to improve ranking, it’s foundational to what we offer.

Glean is sponsoring Digital Transformation Week on 17-18 May 2023 and will be sharing its expertise with attendees. The event is co-located with the AI & Big Data Expo. Swing by Glean’s booth at stand #174.

The post Arvind Jain, Glean: On using AI to surface knowledge appeared first on AI News.

NVIDIA named QuEST an Elite Service Delivery Partner in the NVIDIA Partner Network (NPN) back in June.

Through NPN, QuEST has early access to NVIDIA platforms, software, solutions, workshops, and technology updates. The previous agreement only covered the USA but NVIDIA has now extended the collaboration to Japan.

Masataka Osaki, Japan Country Manager and Vice President of Corporate Sales at NVIDIA, said:

“We are pleased to welcome QuEST as an NPN Elite Partner not only in the US, but also in Japan.

The NPN Elite-level status is reserved for partners who demonstrate a history of expertise in the areas of artificial intelligence, machine learning, and deep learning.

We hope that QuEST’s solutions and services, based on NVIDIA’s AI technology, will further boost the Japanese industry.”

QuEST has wasted no time in taking advantage of the benefits of being an NPN member.

Using NVIDIA DGX systems, QuEST has trained custom vision AI models that are deployed for high-speed edge inference. Customers are able to begin enhancing their operations and decision-making through rapid proof-of-concept deployments. 

Rajeev Nair, Vice President and Head of Japan Business, QuEST Global, commented:

“We are extremely proud that our NPN Elite partner status has been extended to Japan. QuEST is already engaged with key Japanese customers in high-tech, medical devices, power, and automotive domains providing engineering and digital services. 

The NPN partnership will help us further our efforts and provide the best to our customers in Japan.” 

NVIDIA and QuEST have established a deep relationship over the years. QuEST has been part of NVIDIA’s Jetson Partner Ecosystem since 2018 and was one of the first companies to be selected for the NVIDIA Deep Learning Consulting Partnership Program.

In 2019, QuEST debuted a groundbreaking solution to detect lung cancer nodules from CT scans. The solution uses the NVIDIA Jetson platform for deep neural network training and validation to develop models that enhance the accuracy of CT image analysis compared to conventional image processing methods.

“QuEST’s collaboration with NVIDIA in Japan will help accelerate AI-based digital transformation across our customers,” added Nair. “We look forward to working with NVIDIA to spur technology-driven business innovation and growth for customers across industries.”

(Photo by Jase Bloor on Unsplash)

Find out more about Digital Transformation Week North America, taking place on 9-10 November 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

The post QuEST partners with NVIDIA to deliver next-gen AI solutions in Japan appeared first on AI News.

AI News: What work do you do in the artificial intelligence space? 

Sebastian Santibanez: We understand that the truly successful data-minded organizations are very fluid in their definition of AI and SoftServe has embraced this fluidity by thinking of AI organizations and solutions as those who touch, even transversally, on ML, big data, XR, IoT, robotics and many other advanced technologies. With that said, our AI work spans the full business cycle, from strategic digital consulting to solution design and build to maintenance. Depending on the maturity level of our clients we support them in different ways:

• Clients who are at the beginning of their digital journey get more value when we work together revealing the possibilities of technology. Especially now that a larger share of a business value is linked to certain AI initiatives, our clients trust us for designing a sound digital strategy around their AI-related goals, and conversely, ensuring that their AI dreams advance well anchored to a digital strategy. We see companies who what to start building AI projects before they have a sound strategy and sometimes help them step back and reframe their strategy before going forwards too quickly. Often, building PoCs are part of finding the strategy

• Clients who have taken their first steps already in their digital journey often see more value when SoftServe helps drive their transformation. In this area, we do a lot of work with our clients accelerating their innovation and IP generation; as well as developing their raw ideas into market-ready AI solutions. 

• Clients who are more digitally savvy often engage us to accelerate and optimize their AI-backed initiatives. We normally see extremely valuable market solutions that were created in a semi-artisanal fashion and of course are very hard to optimize and maintain effectively.  This is where our experience in Cloud-AI and XOPs really shines as we are able to transform good AI ideas into well-tuned production machines.

From a technical and organizational point of view, we support our clients with our Centers of Excellence in data science, big data analytics, IoT, XR, robotics, and cybersecurity, in addition to our in-house R&D department and our vast organizational experience in cloud, DevOps, and general software development. We’re prime partners of all major cloud providers and just got awarded Google Cloud Partner of the Year in Machine Learning, we have 10k associates around the world with a very well-established presence in Europe and North America, and a fast-growing presence in the Middle East, Latin America, and Asia.

Transversally, we are known in the market for our obsession with driving measurable solutions. Long ago, we collectively realized that many clients were really struggling with identifying the value potential of their current AI initiatives, or designing AI solutions that drove measurable value, which of course was hurting their stance in front of shareholders and leadership.

Our work in AI and associated technologies goes very deeply into identifying the actual business value of the solutions we design and find ways to effectively measure and communicate the outputs of our clients’ AI initiatives. Historically, clients have been tempted to measure the outcome of their AI solutions in terms of cost savings and revenue increase, which is of course important but certainly not the only metrics that matter.

AI has the tremendous potential of driving a competitive edge by accelerating speed to value when correctly aligned with an organization’s digital journey. We make sure that our clients develop their business with these goals in mind.

AN: What are the latest trends you’ve noticed developing in artificial intelligence and how do you think this will impact businesses and society in general? 

SS: Over the last few years, we have seen a transition in the market from a one-off experimental AI mindset to a more intentional, mature, and business-centred approach to data-backed solutions, which is likely fuelled by the availability of empirical data on what makes AI organizations successful.

Organizations are finding the right recipes to delight their customers and increase loyalty with AI and are investing in the right things: strengthening their data management tools and practices; improving (or even initiating) their data governance programs, better aligning AI initiatives to strategy, and creating a more AI-friendly culture.

We have no doubt that this paradigmatic shift is positive for businesses and for us and our communities. This mature, business-centred approach to AI means that a larger number of optimal solutions will reach the market and will positively affect the lives of billions.

As consumers, we will enjoy access to higher quality, cheaper goods and services which are optimized with AI, and as members of our communities we might see that our essential services such as public transportation, infrastructure or health also become more efficient and affordable for all which, of course, has the added value of reducing the negative impacts of our lifestyles in our planet.

From a more technical point of view, we’re seeing rising expectations of how AI and related technologies like robotics and XR can benefit organizations. Take the case of manufacturers as an example; more of them are accelerating their transition from reactive maintenance to predictive maintenance informed by IoT-Big Data-AI combination, and more of them are also evolving their sample-based quality controls to 100% sample methodologies assisted by computer vision, XR, edge computing, and other technologies.

These new expectations add a burden to AI-adjacent technologies, like IoT or MLOPs because they demand the enablement of heavy workloads at the edge and continuous development and management of algorithms to satisfy very fastly evolving needs, which in turn requires complex containerization and orchestration of physical resources and code across the globe. The industry is, in general, responding well to this challenge and we’re observing a mindset change from creating siloed solutions that are conceived with a focus on one part of the value chain, to a mindset that values convergence of technologies along the value chain.

Clients are also sunsetting their Hadoop clusters and switching back to SQL-based solutions like cloud-native warehouses and distributed query engines, which tremendously help to streamline the cloud-native AI lifecycle. We’re also constantly hearing about the desire to virtualize processes, which is something that Digital Twins, Simulations, reinforcement learning and other data science methods along with sensorization is enabling.

Organizations are using or gearing towards using this virtualization to analyse a variety of scenarios in the safety of the cloud and optimize their real-world operations; not only operations of their physical assets and systems of course, but also process optimization via process twinning, which helps organizations optimize their business workflows. Clients have seen the first wave of successful projects in these areas in the past years and are much more comfortable in investing in these solutions.

If this rising of expectations keeps coming informed by empirical evidence and within the goldilocks zone of the art of the possible, I think the implications for businesses and society in general are going to be very positive. The call to action however is to be very careful in identifying which expectations are rooted in solid evidence and which expectations need to be treated as pie in the sky. Both have their place and need to exist to have a healthy AI market but we can’t let the audience confuse both.

Another aspect we are also starting to see, even if just more recently and not forming a critical mass yet, is an increased awareness on security issues, fairness and explainability in AI.  Executives are starting to understand how fragile some AI solutions can be to attacks that manipulate data in order to change an AI result and are designing their solutions with that added layer of robustness in mind.

Curiously enough, this security awareness seems to have started unidirectionally, from the AI layer towards the data-generating layers, but it hasn’t yet reached the data-generating end of the AI lifecycle; there is still a lot of work to do in the industry so the numerous sensorization efforts are as secure as the cloud workloads.

On the fairness and explainable AI front, policymakers and technologists are coming to terms with some societal implications of trusting AI to make decisions that directly affect people. We are seeing more social actors asking the right questions of “what criteria is this algorithm using to decide on X or Y”, and at the same time, technologists are starting to promote more and more the use of explainable AI models.

As a matter of fact, only in the last year or so, the three largest cloud providers are joining the efforts initiated by IBM a few years back in promoting explainable and fair AI tools. Again, the business and societal implications of these aspects are in general very positive. Safer workloads and transparent analytics mean that life-impacting decisions can be well informed by AI, which is of course in everyone’s best interest. The big caveat will be making sure that technologists and policymakers work together in ensuring that we are able to secure the whole data pipeline, from collection to analytics

AN: The company recently became an advisor and technology partner on UNICEF Ukraine. What does this partnership entail and why did you choose to partner with UNICEF?

SS: We are expanding our strategic partnership with UNICEF Ukraine through 2023. SoftServe will now serve as an advisor and technology partner on UNICEF Ukraine’s projects working toward the goals of sustainable development for children. We have outlined opportunities for cooperation in software development and other activities to support UNICEF programs in Ukraine in education, health, child protection, social policy, communication for development, and others.

Our partnership with UNICEF Ukraine began in April 2020. To date, we have implemented numerous initiatives, including a platform for collecting and analyzing COVID-19 statistics in Ukraine, the launch of the country’s National Volunteer Platform, a web portal dedicated to reforming Ukraine’s school nutrition system, an infant care app for young parents, and an evidence-based medicine website. In 2021, SoftServe will also work on updating the national vaccination portal.

UNICEF’S projects in Ukraine systematically address social issues in child protection. The goal of these initiatives – to enable talented people to change the world – aligns perfectly with SoftServe’s mission.

AN: The company has also become an official member of the United Nations (UN) Global Compact. What do you hope to achieve as part of the Global Compact?

SS: It’s an opportunity for us to become part of the global movement of companies that are changing the world for the better and it’s a new step for us in creating a sustainable business. We are committed to the UN Global Compact initiative and its principles in the areas of human rights, labour, the environment, and anti-corruption. 

Our cooperation with the UN began in 2019. We participated in the ‘Hack for Locals’ hackathon that aimed to develop creative digital solutions to solve problems in local communities.

This year, we joined ‘Co-create with Locals’, the pilot program for the United Nations Development Programme (UNDP), which aims to engage activists in developing innovative solutions in public safety and social cohesion and will be implemented on SoftServe’s Innovation Platform.

AN: Finally, what other notable latest developments have there been recently at SoftServe?

SS: SoftServe surpassed ten thousand employees, a significant milestone, as of July 2021. Our headcount has grown by 26% since the beginning of the year thanks to the growing demand for digital services and an expanding customer base.

SoftServe also won the 2020 Google Cloud Global Specialization Partner of the Year – Machine Learning award.

Finally, SoftServe appointed Adriyan Pavlykevych as Chief Information Security Officer (CISO) as of June 2021. Pavlykevych has almost 20 years of experience with SoftServe. As CISO, he will be responsible for shaping and implementing SoftServe’s information governance and security strategy, including ensuring the secure delivery of the company’s engineering services and maintaining and developing its cyber defense capabilities.

(Photo by Cytonn Photography on Unsplash)

Santibanez will be sharing his invaluable insights during this year’s AI & Big Data Expo Global, which runs from 6-7 September 2021. Find out more about his sessions and how to attend here.

The post Sebastian Santibanez, SoftServe: On helping enterprises successfully use AI in their digital transformations appeared first on AI News.

Catching problems during production enables intervention before materials are wasted—incurring direct and environmental costs. It also saves on the reputational damage and costs associated with returns/recalls after a defective product is shipped to customers.

The solution uses an AI model trained on images of products with abnormalities. These defects are simulated so images of actual products with issues pulled from a production line aren’t necessary.

Fujitsu tested its technology at its Nagano Plant, which manufactures electronic equipment, and noted a 25 percent reduction in the man-hours needed for inspecting printed circuit boards.

The AI is able to detect issues like frayed threads or defective wiring patterns – with “world-leading accuracy” – in products that are designed to vary individually; such as different colour carpets or electronics parts with different wiring shapes.

Fujitsu’s AI achieved an AUROC (Area Under the Receiver Operating Characteristics) score in excess of 98 percent when applied to products with variations to their normal appearance.

The Japanese tech giant aims to use its AI advancement for the company’s COLMINA (PDF) brand which aims to deliver digital transformation specifically for the manufacturing industry.

(Photo by Clayton Cardinalli on Unsplash)

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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