Project P.I. leverages generative AI and computer vision technologies to detect issues such as damaged products or incorrect colours and sizes before they reach customers. The AI model not only identifies defects but also helps uncover the root causes, enabling Amazon to implement preventative measures upstream. This system has proven highly effective in the sites where it has been deployed, accurately identifying product issues among the vast number of items processed each month.

Before any item is dispatched, it passes through an imaging tunnel where Project P.I. evaluates its condition. If a defect is detected, the item is isolated and further investigated to determine if similar products are affected.

Amazon associates review the flagged items and decide whether to resell them at a discount via Amazon’s Second Chance site, donate them, or find alternative uses. This technology aims to act as an extra pair of eyes, enhancing manual inspections at several North American fulfilment centres, with plans for expansion throughout 2024.

Dharmesh Mehta, Amazon’s VP of Worldwide Selling Partner Services, said: “We want to get the experience right for customers every time they shop in our store.

“By leveraging AI and product imaging within our operations facilities, we are able to efficiently detect potentially damaged products and address more of those issues before they ever reach a customer, which is a win for the customer, our selling partners, and the environment.”

Project P.I. also plays a crucial role in Amazon’s sustainability initiatives. By preventing damaged or defective items from reaching customers, the system helps reduce unwanted returns, wasted packaging, and unnecessary carbon emissions from additional transportation.

Kara Hurst, Amazon’s VP of Worldwide Sustainability, commented: “AI is helping Amazon ensure that we’re not just delighting customers with high-quality items, but we’re extending that customer obsession to our sustainability work by preventing less-than-perfect items from leaving our facilities, and helping us avoid unnecessary carbon emissions due to transportation, packaging, and other steps in the returns process.”

In parallel, Amazon is utilising a generative AI system equipped with a Multi-Modal LLM (MLLM) to investigate the root causes of negative customer experiences.

When defects reported by customers slip through initial checks, this system reviews customer feedback and analyses images from fulfilment centres to understand what went wrong. For example, if a customer receives the wrong size of a product, the system examines the product labels in fulfilment centre images to pinpoint the error.

This technology is also beneficial for Amazon’s selling partners, especially the small and medium-sized businesses that make up over 60% of Amazon’s sales. By making defect data more accessible, Amazon helps these sellers rectify issues quickly and reduce future errors.

(Photo by Andrew Stickelman)

See also: X now permits AI-generated adult content

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A study from SS&C Blue Prism, conducted by Forrester Consulting and published in April, put together a composite organisation representative of five customers interviewed. The conclusion was that, over three years, there were key gains in IA from greater productivity to compliance cost avoidance, to improved employee experience and retention. This represented an overall net present value of $53.4 million (£42.5m) per customer.

Yet this may just be the tip of the iceberg. Dan Segura, enterprise sales manager at SS&C Blue Prism, notes one healthcare client who, in what is described as a conservative estimate, delivered savings of more than $140m overall on cost avoidance and recoup. Another healthcare client delivered a use case with a claimed $43m benefit on its own; a bot which recouped overtime pay for nurses and staff during the pandemic.

“They built it in an afternoon,” Segura explains. “It’s a perfect example of being in the right place at the right time; and having the right skills and technology being ready.”

Many of the technologies which comprise intelligent automation have been around for a long time, such as classic RPA (robotic process automation) or OCR (optical character recognition). SS&C Blue Prism’s document automation, which forms part of the latter, is described as a ‘game-changer’ by Segura. “There’s a lot of these processes, whether it’s going to be executed by a robot or a human,” he says. “First things first, we’ve got to get data off documents.

“Automation is not just doing simple tasks anymore thanks to the introduction of AI and generative AI” he adds. “There’s now more understanding, whether it’s assessing information from documents, information from a message, structuring things that are semi-structured or unstructured, to drive the process or complete the process.”

Segura describes wider business process management (BPM) and process orchestration tool Chorus, meanwhile, as ‘one of the world’s best kept secrets.’ Or, at least, it was; in November analyst Everest Group named the tool as a leader and star performer in its Process Orchestration Products PEAK Matrix.

The tool is now getting leverage outside the traditional finance and insurance fields. “It is how millions and millions of transactions and pieces of work are getting done every day,” says Segura. “We’re now seeing adoption [elsewhere] alongside automation to orchestrate their work and give them that end-to-end work orchestration, visibility, and efficiency gains with whatever they have going on.”

So how does a use case come to life? It is often a mixture of inspiration and perspiration. Where SS&C Blue Prism comes in is to ‘help customers catch lightning’, as Segura puts it. “We’ve all been in that situation where it’s like ‘oh if I were running this place, here’s what I would do’,” he says. “Intelligent automation gives you the opportunity to reimagine your processes and transform how you get work done. Once that light switch turns on, and the initial use case is built, that’s really the secret sauce of SS&C Blue Prism; it’s that realisation and awareness of what intelligent automation can deliver.

“We’re always learning from our customers,” adds Segura. “It’s at their direction because they know their business and processes better than anybody. Combine their business expertise with the transformational power of intelligent automation and its digital workforce, then that’s where the magic happens.”

Any organisation, argues Segura, regardless of the industry, has change agents and citizen builders in waiting. Don’t think that’s a misnomer; the term is definitely ‘builder’.

“I hear about these citizen developer programmes, and they’ll say, ‘here we have 500, 1000 citizen developers.’ What I don’t hear is, ‘and with this army of citizen developers we’ve achieved this’,” says Segura. “Whereas I have customers where two people have basically become citizen builders with more of a robust type of approach.” The $43m healthcare single use case is a case in point. “It is the whole mantra of SS&C Blue Prism,” adds Segura. “We’re designed to go after those higher value chain automations that can have a tangible impact on some of the company’s key objectives.”

So, you have the idea, the value proposition, and the capability to build it out. How do you make it stick?  Every organisation is different; though if your company has a continuous process improvement department then that can be a good place to start. Segura likens it to offshoring processes. “You don’t just wave it goodbye and never think about it again,” he explains. “At the end of the day, it still has to function.

“You’re not just ‘digital-shoring’ [automation] and it will essentially be taken care of by digital. Someone has to continuously improve the process; someone has to mind when something changes with the business rules or regulatory compliance; somebody has to be responsible for making sure that those changes are kept up in an agile way.”

SS&C Blue Prism has a longstanding, large US retail customer that combines that lightning capture with the right internal culture around automation. This is a company that has 72,000 employees, as well as 60 ‘digital workers’ executing more than 150 automations. One such automation, through using OCR technology, lets the company automate the processing of inbound customer orders received by digital fax.

The overall result is 6.2 million transactions processed to date, and 250,000 hours of work returned to the business. But there is one extra ingredient required, particularly for a big company: discipline.

“It took them a while to get to that point in maturity,” explains Segura. “They do have a very central function when it comes to the intelligent automation team, [but] keep in mind one of those processes is in supply chain. That process is regularly reviewing 4.2 million purchase orders; it’s minding 50 million inventory case volume; it’s going through two million SKUs for 8000 suppliers.

“This is highly iterative, but it’s that process of having that lightning rod to capture the requirements and give people who are not necessarily technical a platform and a methodology to iterate very closely with the intelligent automation team,” adds Segura.

Think of what SS&C Blue Prism does therefore as providing a superhero cape for those who don’t otherwise get the chance to step into the limelight. It is a message the company will look to broadcast at the Intelligent Automation event in Santa Clara on 5-6 June.

“SS&C Blue Prism opens up that door to enable your citizen builders really make an impact and deliver strategic benefits to the company,” says Segura. “You’re not just playing with a pilot, not just fooling around with something; you’re really getting into the strategic objectives of the company.”

Photo by Tara Winstead

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

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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.

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

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

Four of the major new SDKs are:

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

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

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

Accelerating quantum research

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

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

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

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

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

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

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

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

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

(Image Credit: Nvidia)

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

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