Paul O’Sullivan, Senior Vice President of Solution Engineering (UKI) at Salesforce, sheds light on the complexities of this transformative landscape, urging businesses to tread cautiously while embracing the potential of artificial intelligence.

Unprecedented opportunities

Generative AI has stormed the scene with remarkable speed. ChatGPT, for example, amassed 100 million users in a mere two months.

“If you put that into context, it took 10 years to reach 100 million users on Netflix,” says O’Sullivan.

This rapid adoption signals a seismic shift, promising substantial economic growth. O’Sullivan estimates that generative AI has the potential to contribute a staggering £3.5 trillion ($4.4 trillion) to the global economy.

“Again, if you put that into context, that’s about as much tax as the entire US takes in,” adds O’Sullivan.

One of its key advantages lies in driving automation, with the prospect of automating up to 40 percent of the average workday—leading to significant productivity gains for businesses.

The AI trust gap

However, amid the excitement, there looms a significant challenge: the AI trust gap. 

O’Sullivan acknowledges that despite being a top priority for C-suite executives, over half of customers remain sceptical about the safety and security of AI applications.

Addressing this gap will require a multi-faceted approach including grappling with issues related to data quality and ensuring that AI systems are built on reliable, unbiased, and representative datasets. 

“Companies have struggled with data quality and data hygiene. So that’s a key area of focus,” explains O’Sullivan.

Safeguarding data privacy is also paramount, with stringent measures needed to prevent the misuse of sensitive customer information.

“Both customers and businesses are worried about data privacy—we can’t let large language models store and learn from sensitive customer data,” says O’Sullivan. “Over half of customers and their customers don’t believe AI is safe and secure today.”

Ethical considerations

AI also prompts ethical considerations. Concerns about hallucinations – where AI systems generate inaccurate or misleading information – must be addressed meticulously.

Businesses must confront biases and toxicities embedded in AI algorithms, ensuring fairness and inclusivity. Striking a balance between innovation and ethical responsibility is pivotal to gaining customer trust.

“A trustworthy AI should consistently meet expectations, adhere to commitments, and create a sense of dependability within the organisation,” explains O’Sullivan. “It’s crucial to address the limitations and the potential risks. We’ve got to be open here and lead with integrity.”

As businesses embrace AI, upskilling the workforce will also be imperative.

O’Sullivan advocates for a proactive approach, encouraging employees to master the art of prompt writing. Crafting effective prompts is vital, enabling faster and more accurate interactions with AI systems and enhancing productivity across various tasks.

Moreover, understanding AI lingo is essential to foster open conversations and enable informed decision-making within organisations.

A collaborative future

Crucially, O’Sullivan emphasises a collaborative future where AI serves as a co-pilot rather than a replacement for human expertise.

“AI, for now, lacks cognitive capability like empathy, reasoning, emotional intelligence, and ethics—and these are absolutely critical business skills that humans need to bring to the table,” says O’Sullivan.

This collaboration fosters a sense of trust, as humans act as a check and balance to ensure the responsible use of AI technology.

By addressing the AI trust gap, upskilling the workforce, and fostering a harmonious collaboration between humans and AI, businesses can harness the full potential of generative AI while building trust and confidence among customers.

You can watch our full interview with Paul O’Sullivan below:

Paul O’Sullivan and the Salesforce team will be sharing their invaluable insights at this year’s AI & Big Data Expo Global. O’Sullivan will feature on a day one panel titled ‘Converging Technologies – We Work Better Together’.

The post Paul O’Sullivan, Salesforce: Transforming work in the GenAI era appeared first on AI News.

At its core, Predibase is a declarative machine learning platform that aims to streamline the process of developing and deploying machine learning models. The company is on a mission to simplify and democratise machine learning, making it accessible to both expert organisations and developers who are new to the field.

The platform empowers organisations with in-house experts, enabling them to supercharge their capabilities and reduce development times from months to just days. Additionally, it caters to developers who want to integrate machine learning into their products but lack the expertise.

By using Predibase, developers can avoid writing extensive lines of low-level machine learning code and instead work with a simple configuration file – known as a YAML file – which contains just 10 lines specifying the data schema.

Predibase reaches general availability

During the expo, Predibase announced the general availability of its platform.

One of the key features of the platform is its ability to abstract away the complexity of infrastructure provisioning. Users can seamlessly run training, deployment, and inference jobs on a single CPU machine or scale up to 1000 GPU machines with just a few clicks. The platform also facilitates easy integration with various data sources, including data warehouses, databases, and object stores, regardless of the data structure.

“The platform is designed for teams to collaborate on developing models, with each model represented as a configuration that can have multiple versions. You can analyse the differences and performance of the models,” explains Molino.

Once a model meets the required performance criteria, it can be deployed for real-time predictions as a REST endpoint or for batch predictions using SQL-like queries that include prediction capabilities.

Importance of low-code in machine learning

The conversation then shifted to the importance of low-code development in machine learning adoption. Molino emphasised that simplifying the process is essential for wider industry adoption and increased return on investment.

By reducing the development time from months to a matter of days, Predibase lowers the entry barrier for organisations to experiment with new use cases and potentially unlock significant value.

“If every project takes months or even years to develop, organisations won’t be incentivised to explore valuable use cases. Lowering the bar is crucial for experimentation, discovery, and increasing return on investment,” says Molino.

“With a low-code approach, development times are reduced to a couple of days, making it easier to try out different ideas and determine their value.”

Trends in LLMs

The discussion also touched on the rising interest in large language models. Molino acknowledged the tremendous power of these models and their ability to transform the way people think about AI and machine learning.

“These models are powerful and revolutionizing the way people think about AI and machine learning. Previously, collecting and labelling data was necessary before training a machine learning model. But now, with APIs, people can query the model directly and obtain predictions, opening up new possibilities,” explains Molino.

However, Molino highlighted some limitations, such as the cost and scalability of per-query pricing models, the relatively slow inference speeds, and concerns about data privacy when using third-party APIs.

In response to these challenges, Predibase is introducing a mechanism that allows customers to deploy their models in a virtual private cloud, ensuring data privacy and providing greater control over the deployment process.

Common mistakes

As more businesses venture into machine learning for the first time, Molino shared his insights into some of the common mistakes they make. He emphasised the importance of understanding the data, the use case, and the business context before diving headfirst into development. 

“One common mistake is having unrealistic expectations and a mismatch between what they expect and what is actually achievable. Some companies jump into machine learning without fully understanding the data or the use case, both technically and from a business perspective,” says Molino.

Predibase addresses this challenge by offering a platform that facilitates hypothesis testing, integrating data understanding and model training to validate the suitability of models for specific tasks. With guardrails in place, even users with less experience can engage in machine learning with confidence.

The general availability launch of Predibase’s platform marks an important milestone in their mission to democratise machine learning. By simplifying the development process, Predibase aims to unlock the full potential of machine learning for organisations and developers alike.

You can watch our full interview with Molino below:

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

The post Piero Molino, Predibase: On low-code machine learning and LLMs appeared first on AI News.

AI News caught up with Vincent Chio, Data Science Lead at Shopify, to hear what the company is doing in the space and how AI is improving the end-to-end retail experience.

AI News: How has AI changed the shopping experience in recent years for both sellers and buyers?

Vincent Chio: From automated marketing, to smart fulfilment, and predictive analytics, AI has made it easier for retailers to manage and grow their business. But not every retailer has the resources to build and implement AI in their business.

That’s why at Shopify, we really focus on bringing the power of AI—which has historically been reserved for enterprise businesses—into the hands of our merchants, who are businesses of all sizes. To provide a few examples of what that looks like, we use machine learning in our fulfilment network to predict the closest fulfilment centres and optimal inventory quantities per location to ensure fast, low-cost delivery of our merchants’ products. Our business chat app, Shopify Inbox, is built on a natural language processing foundation that helps merchants convert conversations into sales. And, through state-of-the-art machine learning models that predict merchant business success, our product Shopify Capital automatically sends our merchants funding offers without them having to apply.

How this shows up for and benefits buyers is in the form of more accessible, seamless and personalized shopping experiences. From targeted product recommendations, to faster shipping and better ways to connect, buyers expect more from retailers, and AI is helping retailers meet those expectations. 

AN: What are some of the hurdles you’ve faced in bringing an AI model into production and how did you overcome them?

VC: When it comes to shipping anything, there are always hurdles you’re bound to face. At Shopify, we follow a few guiding principles for implementing and scaling AI that ensure easy adoption from the get-go. 

At Shopify, we take a merchant-first approach to identifying problem areas. So our first principle for implementing and scaling AI is to make sure that what we’re building is solving a merchant problem, and that we have enough data to create a solution. 

Second, we start simple. If a regression model will solve our merchant problem, that’s where we start. This doesn’t mean we avoid building complex models, it just means that we first prove that we can use AI/ML to solve the problem, and then we iterate by building complex models. 

These two steps are key for getting stakeholder buy-in which, if you don’t get, can stop your project before it even gets off the ground. We’ve got more principles and tips that you can check out here. 

AN: An increasing number of third-party integrations are available for Shopify that harness AI—what are some of the most unique and/or interesting ones in your view?

VC: We’ve got a ton of third-party developers creating innovative apps that help extend the capabilities of our merchants’ stores. Some of the most interesting ones, in my opinion, are the ones using AI to translate those authentic, in-person retail experiences to online stores. Like I mentioned earlier, AI is changing the shopping experience by using data to bring more personalization to retail.

Our third-party apps that use algorithms to help merchants optimize the full buyer journey, from marketing and conversational automation, to recommendations and cart abandonment, not only have the power to help merchants convert, but they’re creating better shopping experiences for buyers.

AN: Shopify introduced LinNét earlier this year, its new product categorisation model. How does the new model differ from its predecessor and why was it deemed necessary?

VC: Shopify has seen amazing merchant growth in the past 2 years, hitting over 1.7 million merchants across the world. New merchants means new products, so we decided to reevaluate our existing product categorization model. We wanted to evaluate our old model’s performance because it’s important that we understand what our merchants are selling, so we can build the best products that help power their business growth.

After evaluating key metrics like how often our predictions were correct and how often do we provide a prediction, while also taking a look at our product road map and how our model might support new products, we decided to build a new model to improve our performance. 

Compared to our old model, LinNét not only uses text features for prediction but also images. On top of this, LinNét has the ability to understand products in multiple languages. LinNét was also part of a larger effort to modernize our machine learning systems and we can now do things like real-time prediction, which we couldn’t do with the previous model.

With these new features, LinNét has increased our leaf precision by 8% while doubling our coverage. If you’re interested in learning more, read our blog!

AN: What’s next for AI at Shopify?

VC: We’re excited to further leverage the scale of our data to not only empower Shopify but to create new experiences for our merchants that are impossible without data.

Some of the ways we’re doing that is by exploring how to better support merchant workflows through product understanding and creating experiences for merchants that suggest best actions for their workflows, while foregrounding merchant autonomy.

How that will show up is through eliciting merchant feedback through accepting or rejecting our recommendations, and through education around our machine learning approaches to workflow optimization.

AN: Shopify is sponsoring, speaking, and exhibiting at this year’s AI & Big Data Expo Europe. What can attendees expect from your presence at the event?

VC: Attendees can expect the chance to really get to know the Shopify Data Science & Engineering team, and the kind of work we do. 

On day one of the expo, you’ll get to hear more from me! Diving into Shopify Inbox and the natural language processing foundation behind the product, I’ll illustrate how we accelerate product development with AI at Shopify, providing takeaways that can be used at any organization. I’ll also cover how to build a data foundation to establish trust and identify opportunities only AI can solve at scale.

Then, in the afternoon, you’ll have the chance to hear from Shopify’s Yizhar Toren, Senior Data Scientist. Yizhar will join a panel conversation on ramping up AI projects, discussing key tips like how to move your project from experimentation to production, and turning AI into ROI.

Attendees will also get the chance to meet our speakers and recruiters at our booth on the exhibition floor. Come say hi at booth #301!

(Photo by Mike Petrucci on Unsplash)

Shopify will be sharing its invaluable insights during this year’s AI & Big Data Expo Europe which runs from 23-24 November 2021. Shopify’s booth number is 301. Find out more about the event here.

The post Vincent Chio, Shopify: On using AI to revolutionise the retail industry appeared first on AI News.

AI plays a key role in making what BMC Software calls an ADE (Autonomous Digital Enterprise). These cutting-edge businesses know how to wield the latest technologies to minimise manual tasks and maximise the use of uniquely human talents.

AI News joined Ram Chakravarti, CTO of BMC Software, to discuss autonomous digital enterprises and the growing demand for AIOps.

AI News: BMC coined the phrase “Autonomous Digital Enterprise” (ADE). What makes an ADE and what competitive benefits does it present?

Ram Chakravarti: An Autonomous Digital Enterprise (ADE) is made up of intelligent, interconnected, tech-enabled, value-creating systems that minimise manual effort to capitalise on human creativity, skills, and intellect across the enterprise. Transitioning to an ADE presents a variety of benefits for organisations, enabling them to rapidly evolve, and ultimately stay competitive through streamlined agility, customer centricity, and actionable insights. 

One of the key benefits is automation, working as a business function alongside humans, to execute tasks faster and reduce the risk of errors. This means that employees are no longer burdened with manual tasks and can focus on higher-value activities. In addition, an ADE optimises costs, while significantly enhancing customer satisfaction. To support the evolution to an ADE, organisations need to embrace new approaches to talent management, evolve IT departments and optimise technology buying.

AN: Why do we need AIOps and what are its benefits?

RC: There are numerous benefits that come with implementing AIOps. For starters, AIOps provides organisations with insights into all layers of the IT environment. Monitoring and maintaining large complex systems is becoming too much for humans alone. As an example, the volume of operational metrics and log data being created is overwhelming, which can drastically slow repair times when a problem does arise. By applying machine learning techniques and training the models using historical data, AIOps can analyse reams of performance data to identify potential issues, allowing IT teams to take precautionary maintenance measures before a problem impacts customers. An intelligent AIOps solution essentially automates manual processes, meaning organisations will begin to see an increase in employee satisfaction, productivity and customer retention, while significantly saving time and resources.

AN: Around a decade ago, DevOps was a new and barely understood term that has since become somewhat critical to success. Do you think we’ll look back on AIOps in the same way by the end of this decade?

RC: By the end of the decade, we can expect to see the demand for AIOps continue to grow as intelligent automation is added to the mix to help fix problems without human involvement, which I refer to as actionability. Additionally, the intelligence and predictive analytics capabilities of AIOps can be applied beyond traditional IT data: Consider operational data that’s generated by wind turbines, cell towers, trucking fleets. All of that data can be analysed to help predict physical failures and improve maintenance schedules. Noisy datasets will be a barrier of the past as smarter strategies and centralised AIOps solutions help organisations improve the customer experience, deliver on modern application assurance and optimisation, and coupled with intelligent automation to help organisations thrive as ADEs.

AN: What data can be obtained from sources like IoT devices, customer engagement systems, and social media, and how can such huge amounts of data be converted into actionable insights?

RC: By 2025, we can expect data to be reaching unprecedented levels, meaning essentially every company will become a technology-driven business. As noted above, industrial IoT can be used for predictive maintenance and improve monitoring of physical systems. Customer engagement and social media data are all part of delivering a transcendent customer experience. The ability to quickly aggregate, scrub, and analyse your data is key. It paves the way for differentiated business data with key insights across your technology, tools, and employees which then make it possible to deliver a powerful, personalised customer experience.

AN: BMC is a sponsor of this year’s AI and Big Data Expo Global in London and will also be hosting a keynote at the event. What insights do you plan on sharing with the audience?

RC: At the AI and Big Data Expo our focus will be on how enterprises are continuing to amass data at exponential rates, why it’s becoming more imperative to unlock its value and how traditional approaches to data and analytics transformations have seen high failure rates. In my session, I look forward to sharing a recipe for success that rapidly turns new insights into fully operationalised production deliverables – all designed to unlock tangible business value from data.

(Photo by Bill Oxford on Unsplash)

Ram Chakravarti will be sharing his invaluable insights during a keynote on day two of AI and Big Data Expo Global, which runs from 6-7 September 2021. Find out more about the event and how to attend here.

The post Ram Chakravarti, CTO, BMC Software: On the benefits of AIOps and autonomous digital enterprises appeared first on AI News.