Introducing lightweight, industry-specific AI models for individual licence, SAS hopes to equip organisations with readily deployable AI technology to productionise real-world use cases with unparalleled efficiency.

Chandana Gopal, research director, Future of Intelligence, IDC, said: “SAS is evolving its portfolio to meet wider user needs and capture market share with innovative new offerings,

“An area that is ripe for SAS is productising models built on SAS’ core assets, talent and IP from its wealth of experience working with customers to solve industry problems.”

In today’s market, the consumption of models is primarily focused on large language models (LLMs) for generative AI. In reality, LLMs are a very small part of the modelling needs of real-world production deployments of AI and decision making for businesses. With the new offering, SAS is moving beyond LLMs and delivering industry-proven deterministic AI models for industries that span use cases such as fraud detection, supply chain optimization, entity management, document conversation and health care payment integrity and more.

Unlike traditional AI implementations that can be cumbersome and time-consuming, SAS’ industry-specific models are engineered for quick integration, enabling organisations to operationalise trustworthy AI technology and accelerate the realisation of tangible benefits and trusted results.

Expanding market footprint

Organisations are facing pressure to compete effectively and are looking to AI to gain an edge. At the same time, staffing data science teams has never been more challenging due to AI skills shortages. Consequently, businesses are demanding agility in using AI to solve problems and require flexible AI solutions to quickly drive business outcomes. SAS’ easy-to-use, yet powerful models tuned for the enterprise enable organisations to benefit from a half-century of SAS’ leadership across industries.

Delivering industry models as packaged offerings is one outcome of SAS’ commitment of $1 billion to AIpowered industry solutions. As outlined in the May 2023 announcement, the investment in AI builds on SAS’ decades-long focus on providing packaged solutions to address industry challenges in banking, government, health care and more.

Udo Sglavo, VP for AI and Analytics, SAS, said: “Models are the perfect complement to our existing solutions and SAS Viya platform offerings and cater to diverse business needs across various audiences, ensuring that innovation reaches every corner of our ecosystem. 

“By tailoring our approach to understanding specific industry needs, our frameworks empower businesses to flourish in their distinctive Environments.”

Bringing AI to the masses

SAS is democratising AI by offering out-of-the-box, lightweight AI models – making AI accessible regardless of skill set – starting with an AI assistant for warehouse space optimisation. Leveraging technology like large language models, these assistants cater to nontechnical users, translating interactions into optimised workflows seamlessly and aiding in faster planning decisions.

Sgvalo said: “SAS Models provide organisations with flexible, timely and accessible AI that aligns with industry challenges.

“Whether you’re embarking on your AI journey or seeking to accelerate the expansion of AI across your enterprise, SAS offers unparalleled depth and breadth in addressing your business’s unique needs.”

The first SAS Models are expected to be generally available later this year.

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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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.

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

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The result of the collaboration is the Siemens Industrial Copilot, a powerful AI assistant designed to enhance collaboration between humans and machines in the manufacturing sector. The tool enables rapid generation, optimisation, and debugging of complex automation code, significantly reducing simulation times from weeks to minutes.

At the core of this collaboration is the integration of Siemens Industrial Copilot with Microsoft Teams, connecting design engineers, frontline workers, and various teams across business functions. This integration simplifies virtual collaboration, empowering professionals with new AI-powered tools and simplifying tasks that previously required extensive time and effort.

Empowering industries with Generative AI

Satya Nadella, Chairman and CEO of Microsoft, expressed the immense potential of this collaboration, stating: “With this next generation of AI, we have a unique opportunity to accelerate innovation across the entire industrial sector.”

Siemens CEO Roland Busch echoed this sentiment; emphasising the revolutionary impact on design, development, manufacturing, and operations.

The companies envision AI copilots becoming integral in industries such as manufacturing, infrastructure, transportation, and healthcare.

Schaeffler AG – a leading automotive supplier – is already embracing generative AI, enabling engineers to generate reliable code for industrial automation systems. Siemens Industrial Copilot will work to reduce downtimes.

Facilitating virtual collaboration

To facilitate virtual collaboration, Siemens and Microsoft are launching Teamcenter for Microsoft Teams; an application that utilises generative AI to connect functions across the product design and manufacturing lifecycle.

This integration will allow millions of workers who previously lacked access to Product Lifecycle Management (PLM) tools to contribute seamlessly to the design and manufacturing processes.

The collaboration between Microsoft and Siemens looks set to be an excellent case study of how AI empowers industries and professionals, revolutionising traditional workflows and fostering global innovation.

(Photo by Sezer Arslan on Unsplash)

See also: Bob Briski, DEPT®:  A dive into the future of AI-powered experiences

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

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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.

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The world’s largest tech exhibition, CES is taking place at the Las Vegas Convention Centre (LVCC) from 5-7 January this year.

MicroAI has partnered with communications solutions provider iBASIS to showcase Launchpad’s management capabilities at booth 12318.

Using connectivity provided by iBASIS, the demo will show how Launchpad manages MicroAI software running on embedded devices and handles data from multiple sensors.

It will also highlight Launchpad’s ability to securely administer a fleet of SIM cards within the same portal, thus simplifying mobile device management for customers.

MicroAI CEO, Yasser Khan, said: “Edge-native AI enables embedded AI software to run on microcontrollers and microprocessors in endpoint devices, transforming how AI can be made available right where data is captured.

Launchpad provides a straightforward way for companies to manage this – opening up new opportunities across many industry sectors.”

The company’s new security software will also be on show at its booth. MicroAI Security uses a proprietary embedded AI algorithm to detect, alert, and visualise cyber security attacks in real-time, running directly on edge and endpoint connected devices.

Use cases range from standard cyber attack mitigation to protecting critical assets, IoT devices, and industrial systems.

MicroAI will be demonstrating how its software can be used by manufacturers at the Trump International Tower a mile west of the LVCC.

By collaborating with KDDI, who are providing an LTE network for the system, MicroAI will show how its software enables data from sensors in a factory to be analysed by edge AI algorithms.

MicroAI Grid then enables a manufacturer to link this with multiple sites around the world, automatically sharing data and intelligence.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo. The next events in the series will be held in Santa Clara on 11-12 May 2022, Amsterdam on 20-21 September 2022, and London on 1-2 December 2022.

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The pesky laws of physics have been used by engineers for centuries to work out – using complex equations – the stresses the materials they’re working with are being put under. It’s a time-consuming but vital task to prevent structural failures which could be costly at best or cause loss of life at worst.

“Many generations of mathematicians and engineers have written down these equations and then figured out how to solve them on computers,” says Markus Buehler, the McAfee Professor of Engineering, director of the Laboratory for Atomistic and Molecular Mechanics, and one of the paper’s co-authors.

“But it’s still a tough problem. It’s very expensive — it can take days, weeks, or even months to run some simulations. So, we thought: Let’s teach an AI to do this problem for you.”

By employing computer vision, the AI tool developed by MIT’s researchers can generate estimates of material stresses in real-time.

A Generative Adversarial Network (GAN) was used for the breakthrough. The network was trained using thousands of paired images—one showing the material’s internal microstructure when subjected to mechanical forces, and the other labelled with colour-coded stress and strain values.

Using game theory, the GAN is able to determine the relationships between the material’s appearance and the stresses it’s being put under.

“From a picture, the computer is able to predict all those forces: the deformations, the stresses, and so forth,” Buehler adds.

Even more impressively, the AI can recreate issues like cracks developing in a material that can have a major impact on how it reacts to forces.

Once trained, the neural network can run on consumer-grade computer processors. This makes the AI accessible in the field and enables inspections to be carried out with just a photo.

You can find a full copy of the paper here.

(Photo by CHUTTERSNAP 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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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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The global engineering giant will test key features of Fetch.ai’s testnet until the end of this month and will deploy a node on the network. The strategic engineering project between Fetch.ai and Bosch is called the Economy of Things (EoT).

Dr Alexander Poddey, the leading researcher for digital socio-economy, cryptology, and artificial intelligence in the EoT project, said:

“Our collaboration with Fetch.ai spans from the aspects of governance and orchestration of DLT-based ecosystems, multi-agent technologies to collective learning.

They share our belief that these elements are crucial to realising the economic, social, and environmental benefits of IoT technologies.”

Fetch.ai’s testnet launched in October 2020 and the firm is now gearing up for its mainnet launch in March. The company has been ramping up announcements in advance of the mainnet launch and just last week announced a partnership with FESTO to launch a decentralised marketplace for manufacturing.

After the mainnet launch, Bosch intends to run nodes and applications on Fetch.ai’s blockchain network.

Jonathan Ward, CTO of Fetch.ai, commented:

“We have been working with Bosch for some time towards our shared vision of building open, fair, and transparent digital ecosystems. I’m delighted to be able to announce the first public step in bringing these technologies into the real world.

We’re looking forward to working further with Bosch to bring about the wide adoption of these ground-breaking innovations, which will hugely benefit consumers and businesses in many industries including automotive, manufacturing, and healthcare.” 

Fetch.ai is working on decentralised autonomous “agents” which perform real-world tasks. 

Bosch is attracted to Fetch.ai’s vision of collective learning technologies and believes it can be a key enabler in their plans for AI-enabled devices—allowing AI agents to be trained which operate within smart devices while preserving users’ privacy and control of their data.

Fetch.ai’s vision is bold but it has the team and partnerships to pull it off. The company’s roster features talent with experience from DeepMind, Siemens, Sony, and a number of esteemed academic institutions.

Bosch has long expressed a keen interest in distributed ledger technologies and established multiple industry partnerships.

The venture capital arm of Bosch, Robert Bosch Venture-Capital, invested in the IOTA Foundation. Bosch later patented an IOTA-based digital payments system and recently financially supported a hackathon for the DLT platform which uses a scalable DAG (Directed Acyclic Graph) data structure called the ‘Tangle’ in a bid to overcome some of the historic problems with early blockchains.

Fetch.ai and IOTA are in the same space but have different goals, it’s not a choice of one or the other. Companies like Bosch can take advantage of the exciting potential offered by both DLTs to gain a competitive edge.

(Photo by Adi Goldstein 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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Fetch.ai is based in Cambridge, UK and has built an impressive team of talent with experience from DeepMind, Siemens, Sony, and a number of esteemed academic institutions. The company is working on decentralised autonomous “agents” which perform real-world tasks.

FESTO was founded in 1925 and currently produces and sells pneumatic and electrical control and drive technology. The company has continued to thrive over the years through consistent reinvention and likes to show off around once a year by developing bionic robots like its ‘flying fox’ which uses machine learning to optimise its flight behaviour with every manoeuvre:

The partnership with Fetch.ai is FESTO’s first foray into blockchain and shows how the company aims to remain at the cutting-edge of technological developments.

Fetch.ai will develop a decentralised manufacturing marketplace for FESTO to help transform the company’s existing control systems and make them more efficient.

Maria Minaricova, Director of Business Development at Fetch.ai, said:

“We are delighted to be able to announce a collaboration with such an advanced technology company like FESTO.

With FESTO’s contribution, we will be able to demonstrate in real life the benefits of autonomous AI agents in manufacturing and supply chain.

We look forward to working further with FESTO to bring about the wide adoption of these ground-breaking manufacturing innovations.”

The marketplace will make use of Fetch.ai’s technology stack and its ‘Multi-Agent’ architecture. FESTO hopes the marketplace will help to avoid some of the challenges with existing centralised manufacturing process likes demand fluctuations and uneven utilisation of capacity.

Fetch.ai explained the advantages of switching to a decentralised solution:

“The advantages of an agent-based-approach within a decentralised manufacturing framework have the potential to lead to supply-chain optimisation based on real-time information, increasing the responsiveness of the enterprise to the market requirements, a higher degree of autonomy in manufacturing, and delivering personalised, tailored orders to customers.”

A report from TrendMicro last year found that 65 percent of manufacturing environments are using outdated systems even when more secure and efficient options are available.

“Industry 4.0 offers unparalleled opportunities to increase productivity, enhance process efficiencies, and realise on-demand manufacturing,” said Steve Quane, Executive Vice President, Network Defense and Hybrid Cloud Security for Trend Micro.

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.

The post Fetch.ai partners with FESTO on decentralised manufacturing marketplace appeared first on AI News.

Big data and artificial intelligence (AI) power everything that the business does – the global director of digital innovation, Greg Chambers, said: “Artificial intelligence is the foundation for everything we do. We create intelligent experiences. Artificial intelligence is the kernel that powers that experience.”

What Problem Is Artificial Intelligence Helping To Solve?

Marketing soft drinks around the world is not a “one-size-fits-all affair”. Coca-Cola products are  marketed and sold in over 200 countries.

In each of these markets there are local differences concerning flavours, sugar and calorie contents, marketing preferences and competitors faced by the brand.

This means that to stay on top of the game in every territory, it must collect and analyse huge amounts of data from disparate sources to determine which of its 500 brands are likely to be well received. The taste of their most well-known brands will even differ from country to country, and understanding these local preferences is a hugely complex task.

How Is Artificial Intelligence Used In Practice?

Coca-Cola serves a large number of its drinks every day through vending machines. On newer machines, typically the customer will interact through a touch-screen display, enabling them to select the product they want and even customise it with “shots” of different flavours. The company has begun fitting these machines with AI algorithms allowing them to promote drinks and flavours that are most likely to be well received in the specific locations where they are installed.

The vending machines can even alter their “mood” depending on where they are located – with machines in a shopping mall displaying a colourful, fun persona, those in a gym more focused on achieving performance, and those in a hospital appearing more functional.

Coca-Cola also uses AI to analyse social media and understand where, when and how its customers like to consume its products, as well as which products are popular in particular localities. With over 90% of consumers making purchasing decisions based on social media content, understanding how its billions of customers are discussing and interacting with the brand on platforms like Facebook, Twitter and Instagram is essential to its marketing strategy. To do this, Coca-Cola analysed engagement with over 120,000 pieces of social content to understand the demographics and behavior of its customers and those discussing the products.

Another application of AI was in securing proof of purchase for the company’s loyalty and reward schemes. When customers were asked to manually enter 14-digit product codes printed on bottle caps into websites and apps to verify their purchases, uptake was understandably low due to the unwieldy nature of the operation.

To encourage more customers to engage with these schemes, Coca-Cola worked to develop image recognition technology that allows purchases to be verified by taking a single smartphone picture.

What Technology, Tools And Data Were Used?

Coca-Cola collects data on local drink preferences through the interfaces on its touch-screen vending machines – over 1 million of them are installed in Japan alone.

To understand how its products are discussed and shared on social media, the company has set up 37 “social centers” to collect data and analyse it for insights using the Salesforce platform. The aim is to create more of the content that is shown to be effective at generating positive engagement. In the past, the process of creating this content was carried out by humans; however, the company has been actively looking at developing automated systems that will create adverts and social content informed by social data.

It also uses image recognition technology to target users who share pictures on social media inferring that they could be potential customers. In one example of this strategy in action, Coca-Cola targeted adverts for its Gold Peak brand of iced tea at those who posted images that suggested they enjoy iced tea, or in which the image recognition algorithms spotted logos of competing brands. Once the algorithms determined that specific individuals were likely to be fans of iced tea, and active social media users who shared images with their friends, the company knows that targeting these users with adverts is likely to be an efficient use of their advertising revenue.

For purchase verification, off-the-shelf image recognition technology proved to be insufficient for reading the low-resolution dot matrix printing used to stamp product codes onto packaging. So, Coca-Cola worked to develop its own image recognition solution using Google’s TensorFlow technology. This used convolutional neural networks to enable machine recognition of codes that could often appear differently depending on when and where they were printed.

What Were The Results?

Analysis of the data from vending machines by AI algorithms allows Coca-Cola to more accurately understand how the buying habits of its billions of customers varies across the globe.

It uses this to inform new product decisions – for example, the decision to launch Cherry Sprite as a bottled product in the United States was taken because the data showed that this was likely to be a winning initiative.

Computer vision analysis and natural language processing of social media posts, as well as deep learning-driven analysis of social engagement metrics, allows Coca-Cola to produce social advertising that is more likely to resonate with customers and drive sales of its products.

Applying TensorFlow to create convolutional neural networks enabled scanners to recognise product codes from a simple photograph, increasing customer engagement with Coca-Cola’s different loyalty programs around the world.

Key Challenges, Learning Points And Takeaways

• If you sell hundreds of different products across multiple countries, perceptions and customer behaviour can vary greatly from market to market. Understanding these differences helps tailor specific messages for different markets, rather than relying on a one-size-fits-all approach
• When you’re dealing with global brands, user data from social media or generated through your own systems (such as vending machines) is vast and messy. AI provides a viable method of structuring this data and drawing out insights
• Computer vision technology such as image recognition tools can analyse millions of social media images to help a brand understand when, how and by whom its products are enjoyed
• As well as making marketing decisions, brands that are fully invested in AI are beginning to use it for designing new products and services

This is an edited extract from Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems, by Bernard Marr, with Matt Ward (published by Wiley, April 2019).

About the authors: Bernard Marr is the founder and CEO of Bernard Marr & Co and an internationally best-selling business author, futurist, keynote speaker and strategic advisor to companies and governments. He is one of the world’s most highly respected voices and a renowned expert when it comes to topics such as artificial intelligence and big data. Marr advises many of the world’s best-known organisations on strategy, digital transformation and business performance. He is the author of Big Data in Practice: How 45 Successful Companies used Big Data Analytics to Deliver Extraordinary Results and Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance, both published with Wiley.

Matt Ward is the research lead for Bernard Marr & Co. Matt has a background in investigative journalism and spent the last few years working closely with Bernard Marr on the latest technology topics. Matt is an expert and experienced writer in the field of business technology and artificial intelligence, where he has worked with companies such as IBM, Intel, Citibank and NASA.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

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