In June, the company unveiled its generative AI roadmap, codenamed ‘Project Morpheus,’ with benefits including instant app generation using conversational prompts and an AI-powered app editor offering suggestions across the stack.  The mission remains clear: ‘developer productivity without trade-offs’, as founder and CEO Paulo Rosado puts it.

Project Morpheus, in the words of Nuno Carneiro, OutSystems AI product manager, is ‘the next generation of software development.’ “What we’re doing is building a completely new development experience, based on this premise that AI will give you suggestions. You do not have to code practically anything, and the AI is suggesting what to do,” says Carneiro.

“You have a What-You-See-Is-What-You-Get visual experience in terms of software development where you can change the application directly in your development environment. – On top of that, AI gives you suggestions about what you might want to change so that you don’t need to code things manually.”

This means the artificial intelligence is there to tweak, rather than take over. The company’s main offering in the space to date has been the OutSystems AI Mentor System. From code, to architecture, to performance, the developer is in control, but always has an on-call expert to hand.

Scepticism is naturally there, as it was with the rise of low-code platforms. But having slayed the dragon once before, is the job easier this time? “We see the same patterns of people being sceptical of AI in software development,” explains Carneiro. “We’ve been through this process of educating and showing the value of automation in software development before. We now feel like we’re in a good spot to communicate the current transformation in the industry due to the rise of AI.”

The key factor is that the OutSystems platform guards against some of the less salubrious aspects of artificial intelligence technology. Hallucination – where an AI confidently gives an incorrect response – and creating code riddled with vulnerabilities are just two of the pitfalls which could result if given full control. This is where the parallels between low-code and AI-assisted software development are especially striking; even if the code has been generated by AI, you can visually understand what you are building.

“The solutions we see out there at the moment still don’t solve this problem,” says Carneiro. “Because if AI is just writing a bunch of code automatically, and the person in charge of seeing the code and building it doesn’t understand what’s behind it, that’s not going to be a solution for any serious organisation to use. Low-code solves this problem with its visual development experience and the AI Mentor System constantly checks for security vulnerabilities, no matter who, or what, wrote the code.”

The bottom line for businesses is that AI-based development with a low code platform will allow them to complete projects in weeks which would otherwise take months, or even years, to develop. Carneiro gives a theoretical example of a company who wants to do a proof of concept for a new piece of software managing HR internally; a project which could take a week with OutSystems. For wider transformational projects, such as rebuilding an entire supply chain, it would take a few months maximum.

There is another benefit too for larger firms. “We’ve also seen a lot of clients build Centres of Excellence around low-code software development that they then export to their organisations around the world,” says Carneiro. “Using the AI Mentor System means they can then export this and innovate quickly across their whole business.”

Improving the process of software development is only one aspect of a digital transformation journey, however, with OutSystems committed to enabling businesses to adopt AI themselves. Image recognition is one such use case, or using cognitive services that users can add to their applications to solve business problems from unstructured data. This was factored into one part of the generative AI roadmap update, with a new connector announced for Azure OpenAI, built in partnership with Microsoft, to enable the use of large language models in development. “Part of our roadmap here is to help customers build the foundations for AI adoption in their businesses, so they’re not caught off guard,” notes Carneiro.

OutSystems is participating at AI & Big Data Expo Europe, in Amsterdam on September 26-27, and AI and wider digital transformation journeys will be a major part of the agenda. “A typical digital transformation challenge is to connect different data sources, and that’s another place where we believe OutSystems comes in. We’re at the right spot to help businesses solve this,” explains Carneiro. “We naturally help you connect with different data sources, and it’s something we’ve been optimising over the years to help our customers bring in all types of databases and sources – we have tools that help customers connect to integrations and integrate different data sources.

“These challenges might not be obvious before you embark on an AI adoption journey,” Carneiro adds. “But I’m pretty sure anyone who’s tried will recognise them – and we hope they also recognise that OutSystems is a good partner for that.”

Photo by Marc Sendra Martorell 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 comprehensive event is co-located with Digital Transformation Week.

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The operationalisation of data projects has been a key factor in helping organisations turn a data deluge into a workable digital transformation strategy, and DataOps carries on from where DevOps started. But there is a further Gartner warning: organisations who lack a sustainable data and analytics operationalisation framework by 2024 will see their initiatives set back by up to two years.

Operationalisation needs good orchestration to make it work, as Basil Faruqui, director of solutions marketing at BMC, explains. “If you think about building a data pipeline, whether you’re doing a simple BI project or a complex AI or machine learning project, you’ve got data ingestion, data storage and processing, and data insight – and underneath all of those four stages, there’s a variety of different technologies being used,” explains Faruqui. “And everybody agrees that in production, this should be automated.”

This is where Control-M from BMC, and in particular BMC Helix Control-M comes in. Control-M has been an integral part of many organisations for upwards of three decades, enabling businesses to run hundreds of thousands of batch jobs daily and help optimise complex operations such as supply chain management. But an increasingly complex technological landscape, across on-premises to cloud, as well as a greater usage of SaaS-based orchestration alongside consumption, made it a no-brainer to launch BMC Helix Control-M in 2020.

“CRMs and ERPs had been going the SaaS route for a while, but we started seeing more demands from the operations world for SaaS consumption models,” explains Faruqui.

The upshot of being a mature company – BMC was founded in 1980 – is that many customers have simply extended Control-M into more modern use cases. One example of a large organisation – and long-standing BMC customer – running an extremely complex supply chain is food manufacturer Hershey’s.

Apart from the time-sensitive necessity of running a business with perishable, delicate goods, the company has significantly adopted Azure, moving some existing ETL applications to the cloud, while Hershey’s operations are built on a complex SAP environment. Amid this infrastructure Control-M, in the words of Hershey’s analyst Todd Lightner, ‘literally runs our business.’

Faruqui returns to the stages of data ingestion, storage, processing, and insight to explain how Hershey’s would tackle a significant holiday campaign, or decide where to ship product. “It’s all data driven,” Faruqui explains. “They’re ingesting data from lots of systems of record, that are ingesting data from outside of the company; they’re pulling all that into massive data lakes where they’re running AI and ML algorithms to figure out a lot of these outcomes, and feeding into the analytics layer where business executives can look at dashboards and reports to make important decisions.

“They’re a really good example of somebody who has used orchestration and automation with Control-M as a strategic option for them,” adds Faruqui.

Yet this leads into another important point. DataOps is an important part of BMC’s strategy, but it is not the only part. “Data pipelines are dependent on a layer of applications both above and below them,” says Faruqui. “If you think about Hershey’s, trying to figure out what kind of promotion they should run, a lot of that data may be coming from SAP. And SAP is not a static system; it’s a system that is constantly being updated with workflows.

“So how does the data pipeline know that SAP is actually done and the data is ready for the data pipeline to start? And when they figure out the strategy, all that information needs to go back to SAP because the ordering of raw materials and everything is not going to happen in the data pipeline, it’s going to happen in ERPs,” adds Faruqui.

“So Control-M is able to connect across this layer, which is different from many of the tools that exist in the DataOps space.”

Faruqui is speaking at the AI & Big Data Expo Europe in Amsterdam in September around how orchestration and operationalisation is the next step in organisations’ DataOps journeys. So expect not only stories and best practices on what a successful journey looks like, and how to create data pipeline orchestration across hybrid environments combining multiple clouds with on-prem, but also a look at the future – and according to Faruqui, the complexity is only going one way.

“I think one of the things that will continue to be challenging is there’s just lots of different tools and capabilities that are coming up in the AI and ML space,” he explains. “If you look at AWS, Azure, Google, and you go to their website, and you click on their AI/ML offerings, it is quite extensive, and every event they do, they announce new capabilities and services. So that’s on the vendor side.

“On the customer side, what we’re seeing is they want to rapidly test and figure out which [tools] are going to be of use to them,” Faruqui adds. “So as an orchestration vendor, and orchestration in general within DataOps, this is both the challenge and the opportunity.

“The challenge is you’re going to have to keep up with this because orchestration doesn’t work if you can’t integrate into something new – but the opportunity here is that our customers are asking for this.

“They don’t want to have to reinvent the orchestration wheel every time they go and adopt new technology.”

Photo by Larisa Birta 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 comprehensive event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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CEO Mark Zuckerberg confirmed the seat-shuffling in a Facebook post, stating that teams currently working on generative AI will be pulled together. Detail was light on the scale of projects, but Zuckerberg noted a focus on ‘developing AI personas’ longer term, and experimentations taking place on chat in WhatsApp and Messenger, as well as with images, such as creative Instagram filters and advertising formats. The team will report to chief product officer Chris Cox, as reported by multiple sources.

“In the short term, we’ll focus on building creative and expressive tools,” wrote Zuckerberg. “Over the longer term, we’ll focus on developing AI personas that can help people in a variety of ways.

“We have a lot of foundational work to do before getting to the really futuristic experiences, but I’m excited about all of the new things we’ll build along the way,” he added.

Meanwhile, Snap this week announced the launch of My AI for Snapchat, a chatbot powered by the latest version of ChatGPT. The bot is available as an ‘experimental’ feature for Snapchat+ paid subscribers, and among the potential use cases include recommendations, organisation, and content creation.

The announcements from Meta and Snap serve as another tinder bundle with which to ignite the positioning taking place from big tech around generative AI. As this publication has explored, many of the major players are making moves, from Microsoft, to Google, to Amazon. Not everything has gone smoothly to say the least, but this remains the hottest of hot spaces right now. At MWC, taking place this week in Barcelona, one analyst said AI was being ‘mentioned in relation to pretty much everything.’

The recent blunders experienced by Microsoft and Alphabet – the latter wiping a cool $120 billion off the company’s value – were top of mind for Snap, who took the unusual step of apologising in advance for any foot-in-mouth moments users may experience.

The choice quote reads: “As with all AI-powered chatbots, My AI is prone to hallucination and can be tricked into saying just about anything. Please be aware of its many deficiencies and sorry in advance! While My AI is designed to avoid biased, incorrect, harmful or misleading information, mistakes may occur. Please do not share any secrets with My AI and do not rely on it for advice.”

Picture credit: Pixabay

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.

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The post Meta calls up generative AI squad as Snap releases ChatGPT-powered bot appeared first on AI News.

Hugging Face has as its mission the need ‘to democratise good machine learning, one commit at a time.’ The company is best known for its Transformers library for PyTorch, TensorFlow and JAX, which can support tasks ranging from natural language processing, to computer vision, to audio.

There are more than 100,000 free and accessible machine learning models on Hugging Face, which are altogether downloaded more than one million times per day by researchers, data scientists, and machine learning engineers.

In terms of the partnership, AWS will become the preferred cloud provider for Hugging Face, meaning developers can access tools from Amazon SageMaker, to AWS Trainium, to AWS Inferentia, and optimise the performance of their models for specific use cases at a lower cost.

The need to make AI open and accessible to all is at the heart of this announcement, as both companies noted. Hugging Face said that the two companies will ‘contribute next-generation models to the global AI community and democratise machine learning.’

“Building, training, and deploying large language and vision models is an expensive and time-consuming process that requires deep expertise in machine learning,” an AWS blog noted. “Since the models are very complex and can contain hundreds of billions of parameters, generative AI is largely out of reach for many developers.”

“The future of AI is here, but it’s not evenly distributed,” said Clement Delangue, CEO of Hugging Face, in a company blog. “Accessibility and transparency are the keys to sharing progress and creating tools to use these new capabilities wisely and responsibly.”

Readers of AI News will know of the democratisation of machine learning from the AWS perspective. Speaking in September, Felipe Chies outlined the proposition:

“Many of our API services require no machine learning for customers, and in some cases, end users may not even realise machine learning is being used to power experiences. The services make it really easy to incorporate AI into applications without having to build and train ML algorithms.

“If we want machine learning to be as expansive as we really want it to be, we need to make it much more accessible to people who aren’t machine learning practitioners. So when we built [for example] Amazon SageMaker, we designed it as a fully managed service that removes the heavy lifting, complexity, and guesswork from each step of the machine learning process, empowering everyday developers and scientists to successfully use machine learning.”

This announcement can be seen not just in the context of democratising the technology, but from a competitive standpoint. Microsoft’s moves in the market with OpenAI, and its ChatGPT-influenced Bing – albeit with the odd hiccup – have created waves; likewise Google with Bard, again not entirely error-free. Either way, the stakes for the biggest of big tech have increased and the battle ground for the ‘AI wars’ have intensified. Hugging Face has an existing relationship with Microsoft, announcing an endpoints service to securely deploy and scale Transformer models on Azure in May.

Picture credit: Hugging Face

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.

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Machine learning software, by definition, is self-learning. As users ask more questions of an AI, and the AI provides answers, feedback loops are developed which help the product get stronger and the return on investment become greater. 

“It’s really cool that a proper AI solution is self-learning,” Scott Litman, founder and chief operating officer of AI-powered answer engine Lucy, explains. “The AI is growing with them. If the AI misses, it’s a teachable moment, and [it] will be smarter tomorrow.” 

With generative AI, the stakes are now so much higher. Generative AI is defined as algorithms which can be used to create new content, from text, to code, to audio. ChatGPT, from OpenAI, has understandably garnered a fleet of headlines because it appears to have opened up a world of possibility for content creation.  

Yet it is not all plain sailing. For one, users have delighted in pointing out the fallibilities of ChatGPT, which is fine – it is always learning after all. But other users have spotted the software’s tendency to make up a response if it is unsure. “The smug confidence with which [the] AI asserts totally incorrect information is striking,” the writer Ted Gioia noted. “A con artist could not do better.” 

Lucy’s job is not to make incorrect assertions, but to ‘liberate corporate knowledge’: put simply, get the right answer to the right person at the right time in seconds, regardless of where that answer lives. Much of this will primarily involve sifting through reams of PDFs, PowerPoints and Word documents and point to the most relevant detail, but this liberation can turn up insights in previously forgotten places, such as video training courses. 

With the recent release of Lucy 4, the next generation of its platform, and Lucy Synopsis, there is a further push towards generative AI – but without the drawbacks. Lucy can not only point a user to an answer, but provide a unique two-to-three sentence summary which directly answers the question. Crucially, as Steve Frederickson, director of product management points out, Lucy’s summations are there solely to help the user, not offer a spurious alternative. 

One of the key elements of Lucy 4, again involving the generative AI element, is expanded integration with Microsoft Teams and Slack, where users can mention Lucy in a chat. This reflects not just greater ease of use for employees, but a wider trend around search.  

“One of the things we realised last year was that, along with the inefficiency of searching, people in some cases have given up on the idea of searching,” explains Litman. The result is that users are more likely to fire out a message on the chat apps than waste time on a frustrating scavenger hunt. “Which sometimes works – human intelligence is a great thing,” says Litman. “But if you’re the subject matter expert answering all the questions, you’re constantly being disrupted.” 

“We come at it from our own perspective – we have a core value of experimentation,” adds Frederickson. “Lucy has always had the tenet of going above and beyond search. We hold ourselves to that higher standard.” 

It is best to think of Lucy as like a new employee. No matter how glittering your recruit’s CV is, it will take time for a new starter to get used to the role, the systems, and the culture. But they will get better. Unlike human employees though, Lucy can hit the ground running. Frederickson notes that Lucy’s goal is ultimately to ‘give time back to the world’, and a more intuitive user interface and improved navigation help with this.  

Enhanced collaboration is another important aspect of Lucy 4, and again relates to user behaviour. “What do users do once they’ve found the answer?” notes Frederickson. “Do they grab a quote? Do they share it with co-workers? Do they put it in their deck? What is the destination for this knowledge?” Annotating and adding context within the tool all help to retain the knowledge which has been liberated.  

Ultimately, companies survive and thrive on their data literacy. While it is easy to be attracted to big, expansive projects and technologies, adding generative AI to a slick answer engine will help employees, continually improve ROI – and represents the next generation of knowledge management. 

Find out more about Lucy 4 here.

The post Lucy 4 is moving ahead with generative AI for knowledge management appeared first on AI News.

The Microsoft co-founder was participating in his annual Reddit Ask Me Anything (AMA) session and was asked about major technology shifts. AI, Gates noted, was in his opinion ‘the big one.’ 

“I don’t think Web3 was that big or that metaverse stuff alone was revolutionary, but AI is quite revolutionary,” Gates wrote. 

With regard to generative AI, a specific kind of AI focused on generating new content, from text, to images, to music, Gates was particularly interested. “I am quite impressed with the rate of improvement in these AIs. I think they will have a huge impact,” he wrote.  

Gates added he continues to work with Microsoft so is following this area ‘very closely.’ “Thinking of it in the Gates Foundation context we want to have tutors that help kids learn math and stay interested. We want medical help for people in Africa who can’t access a doctor,” he added. 

Previous missives from Gates have been more optimistic in terms of the impact of the metaverse. At the end of 2021, in his personal blog, Gates noted he was ‘super impressed’ by the improvements with regard to spatial audio in particular. This enables more immersive meetings, where the sound is coming from the direction of a colleague as per face-to-face discussion. “There’s still some work to do, but we’re approaching a threshold where the technology begins to truly replicate the experience of being together in the office,” he wrote at the time. 

Microsoft has been gradually exploring the metaverse as part of its strategy to ‘bridge the digital and physical worlds.’ October saw a partnership with Meta on platform and software to ‘deliver immersive experiences for the future of work and play.’ The company cited Work Trend Index data which showed half of Gen Z and millennials surveyed envisioned doing some of their work in the metaverse in the next two years.

(Image Credit: Kuhlmann /MSC under CC BY 3.0 DE license)

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.

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A recent panel session at the AI & Big Data Expo` in London explored these ethical grey areas, from beating inbuilt bias to corporate mechanisms and mitigating the risk of job losses. 

James Fletcher leads the responsible application of AI at the BBC. His job is to, as he puts it, ‘make sure what [the BBC] is doing with AI aligns with our values.’  He says that AI’s purpose, within the context of the BBC, is automating decision making. Yet ethics are a serious challenge and one that is easier to talk about than act upon – partly down to the pace of change. Fletcher took three months off for parental leave, and the changes upon his return, such as Stable Diffusion, ‘blew his mind [as to] how quickly this technology is progressing.’ 

“I kind of worry that the train is pulling away a bit in terms of technological advancement, from the effort required in order to solve those difficult problems,” said Fletcher. “This is a socio-technical challenge, and it is the socio part of it that is really hard. We have to engage not just as technologists, but as citizens.” 

Daniel Gagar of PA Consulting, who moderated the session, noted the importance of ‘where the buck stops’ in terms of responsibility, and for more serious consequences such as law enforcement. Priscila Chaves Martinez, director at the Transformation Management Office, was keen to point out inbuilt inequalities which would be difficult to solve.  

“I think it’s a great improvement, the fact we’ve been able to progress from a principled standpoint,” she said. “What concerns me the most is that this wave of principles will be diluted without a basic sense that it applies differently for every community and every country.” In other words, what works in Europe or the US may not apply to the global south. “Everywhere we incorporate humans into the equation, we will get bias,” she added, referring to the socio-technical argument. “So social first, technical afterwards.” 

“There is need for concern and need for having an open dialogue,” commented Elliot Frazier, head of AI infrastructure at the AI for Good Foundation, adding there needed to be introduction of frameworks and principles into the broader AI community. “At the moment, we’re significantly behind in having standard practices, standard ways of doing risk assessments,” Frazier added.  

“I would advocate [that] as a place to start – actually sitting down at the start of any AI project, assessing the potential risks.” Frazier noted that the foundation is looking along these lines with an AI ethics audit programme where organisations can get help on how they construct the correct leading questions of their AI, and to ensure the right risk management is in place. 

For Ghanasham Apte, lead AI developer behaviour analytics and personalisation at BT Group, it is all about guardrails. “We need to realise that AI is a tool – it is a dangerous tool if you apply it in the wrong way,” said Apte. Yet with steps such as explainable AI, or ensuring bias in the data is taken care of, multiple guardrails are ‘the only way we will overcome this problem,’ Apte added.  

Chaves Martinez, to an extent, disagreed. “I don’t think adding more guardrails is sufficient,” she commented. “It’s certainly the right first step, but it’s not sufficient. It’s not a conversation between data scientists and users, or policymakers and big companies; it’s a conversation of the entire ecosystem, and not all the ecosystem is well represented.” 

Guardrails may be a useful step, but Fletcher, to his original point, noted the goalposts continue to shift. “We need to be really conscious of the processes that need to be in place to ensure AI is accountable and contestable; that this is not just a framework where we can tick things off, but ongoing, continual engagement,” said Fletcher. 

“If you think about things like bias, what we think now is not what we thought of it five, 10 years ago. There’s a risk if we take the solutionist approach, we bake a type of bias into AI, then we have problems [and] we would need to re-evaluate our assumptions.” 

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.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post AI & Big Data Expo: Exploring ethics in AI and the guardrails required  appeared first on AI News.

These industry applications can be similar to the Industrial Internet of Things (IIoT). Take underground mining, for instance, with its remote control and driverless equipment functions and the need to gather insights on predictive maintenance and energy management. Computers in these conditions must operate reliably under vibration, shock, and hot temperatures.

Seventy-five percent of data is forecasted to process outside the cloud by 2025, according to Gartner. Organizations need features in their servers such as higher performance, less data latency, and remote manageability.

Alongside withstanding extreme conditions, ruggedized edge platforms require various other functionality. The platforms need to perform real-time processing through an array of performance accelerators, offer sufficient storage capacity, and have rich I/O ports to be compatible with new and legacy machines. 

In collaboration with Arrow, Dell’s PowerEdge XR11 and XR12 servers aim to offer enterprise compute capabilities in the harshest edge environments. On the performance side, the XR12 is the more expandable of the two, coming across in its third-generation Intel Xeon scalable processors and GPU options, with support for up to two NVIDIA T4 cards or two of the A100, A10, or A40 GPUs, and flexible I/O choices. Storage for the XR11 and XR12 includes the Intel Optane Persistent Memory 200 series. At 16 inches, the chassis can maintain performance while being less than half the depth of a standard server. 

“The servers help OEM customers address the edge computing challenges faced outside the data center,” Dell notes. “Businesses can move workloads to the network edge and run AI algorithms to analyze and act on data near where it’s generated, reducing latency and providing quicker access to data for real-time decision making, saving time and money.”

By bringing computing power to the edge, innovative enterprises realize that a mix of centralized cloud and distributed edge environments are essential. Meanwhile, innovative vendors can now get data center-class performance right where the action is for their customers’ increasing edge deployments. Arrow can provide customized Dell solutions for enterprises that wish to take the next step, going through the entire lifecycle, from ideation development, prototyping, manufacturing, and global distribution.

The post The need for ruggedised edge: Bringing data centre-class performance closer to your data appeared first on AI News.

Think intelligent automation is for you? In this session you will hear IBM Director of Product Management, Leslie Chau, discuss the following:

• Key use cases for AI and automation and why now is the right time to adopt the technology.
• New innovations in automation and how the market has evolved over the last couple of years.
• Challenges that organizations need to overcome in order to adopt intelligent automation.

Leslie Chau is speaking at the next edition of Digital Transformation Week at the Santa Clara Convention Center 5-6 October, exploring digital labor’s transformative effect on employee productivity. Book tickets here.

The post Leslie Chau, IBM: AI & Automation – Creating the Workforce of the Future appeared first on AI News.

If that is the case, whither artificial intelligence? Writing for Enterprise Talk earlier this month, Swapnil Mishra notes of businesses still being in the ‘AI adolescence’ phase, citing research from Accenture which found 63% of 1,200 companies polled were still experimenting with projects.

“The level of an organisation’s mastery of AI-related capabilities in the proper mix to produce a high performance for customers, shareholders, and employees is referred to as AI maturity,” wrote Mishra. “The ability to master a set of critical competencies in the proper combinations – not just in data and AI but also in organisational strategy, talent, and culture – determines AI’s maturity.”

One of the key aspects in getting digital strategy right, across myriad technologies, is through ROI. For AI, this may be harder to achieve than others, as a recent CIO.com article outlines. “Measuring the success of AI can be subjective… evaluating an AI project is an art as much as developing the AI itself,” the report notes. Valuable KPIs for such projects could include a reduction of false alerts, or automatic removal of excessive privileges, for instance.

This nicely sums up the different plates senior executives need to juggle on a strategic, cultural, and technological perspective – and it is a long-term journey as well.

The AI & Big Data Expo Europe, being held in Amsterdam on September 20-21, and the AI & Big Data Expo North America, being held in Santa Clara on October 5-6, features innovative leading brands who are at the heart of this transformational process in its Enterprise AI conference streams.

In Santa Clara, Daniel Wu, head of AI and ML, commercial banking at JPMorgan, will give a presentation on building AI excellence. “With the right framework and clear focus on key aspects to drive success including organisation, data and analytics capability, technology, and value delivery, the power of AI can be fully unleashed and drive exponential impact across enterprises,” Wu notes. Elsewhere, Dr Satyam Priyadarshy, chief data scientist at Halliburton, will explore how to utilise cloud and AI in the energy sector.

At Amsterdam, Julio Peironcely, global director insights and analytics at Danone, will run through how to move a project from ‘experimentation’ to ‘live’ and turning AI to ROI, while Irakli Beridze, head of the centre for AI and robotics at UNICRI, United Nations, will explore the responsible use of AI in law enforcement.

An area where companies can gain inspiration is through tangible use cases of how AI and ML knits together different data sources to create actionable insights and personalised outcomes. A recent article from Business Chief explores this, with insight from Alec Boere, associate partner for AI and automation, Europe at Infosys Consulting. “With the enablement of a truly personalised experience, AI can deride the gaps that you might have in terms of data volume to drive out customer vectors,” noted Boere.

The Applied Data and Analytics track at AI & Big Data Expo Europe and AI & Big Data Expo North America will give access to organisations who are solving industry problems. At Santa Clara, Craig Materick, senior data strategist at Insight, will give a keynote presentation on driving value from data in a world of risk. Meanwhile, in Amsterdam, James Marshall-Roberts, global digital agronomy development lead at Sygenta, will look at the industry of agriculture, and how data and analytics have ‘proven to be the ultimate game changer’ through real-time crop insights.

AI & Big Data Expo Europe – 20-21 September at RAI, Amsterdam. Book your ticket here

AI & Big Data Expo North America – 5-6 October at Santa Clara Convention Center, California. Book your ticket here

Wherever you look in AI and machine learning, there are plenty of challenges, but there is also plenty to be excited about. The upcoming AI & Big Data Expo events will offer food for thought in terms of innovative ideas and next steps for your organisation. Find out about the event series here.

The post What to look out for at AI & Big Data Expo EU and NA: JPMorgan, Danone, and more appeared first on AI News.