At the core of DEPT®’s approach is the strategic utilisation of large language models. Briski articulated the delicate balance between the ‘pioneering’ and ’boutique’ ethos encapsulated in their tagline, “pioneering work on a global scale with a boutique culture.”

While ‘pioneering’ and ’boutique’ evokes innovation and personalised attention, ‘global scale’ signifies the broad outreach. DEPT® harnesses large language models to disseminate highly targeted, personalised messages to expansive audiences. These models, Briski pointed out, enable DEPT® to comprehend individuals at a massive scale and foster meaningful and individualised interactions.

“The way that we have been using a lot of these large language models is really to deliver really small and targeted messages to a large audience,” says Briski.

However, the integration of AI into various domains – such as retail, sports, education, and healthcare – poses both opportunities and challenges. DEPT® navigates this complexity by leveraging generative AI and large language models trained on diverse datasets, including vast repositories like Wikipedia and the Library of Congress.

Briski emphasised the importance of marrying pre-trained data with DEPT®’s domain expertise to ensure precise contextual responses. This approach guarantees that clients receive accurate and relevant information tailored to their specific sectors.

“The pre-training of these models allows them to really expound upon a bunch of different domains,” explains Briski. “We can be pretty sure that the answer is correct and that we want to either send it back to the client or the consumer or some other system that is sitting in front of the consumer.”

One of DEPT®’s standout achievements lies in its foray into the web3 space and the metaverse. Briski shared the company’s collaboration with Roblox, a platform synonymous with interactive user experiences. DEPT®’s collaboration with Roblox revolves around empowering users to create and enjoy user-generated content at an unprecedented scale. 

DEPT®’s internal project, Prepare to Pioneer, epitomises its commitment to innovation by nurturing embryonic ideas within its ‘Greenhouse’. DEPT® hones concepts to withstand the rigours of the external world, ensuring only the most robust ideas reach their clients.

“We have this internal project called The Greenhouse where we take these seeds of ideas and try to grow them into something that’s tough enough to handle the external world,” says Briski. “The ones that do survive are much more ready to use with our clients.”

While the allure of AI-driven solutions is undeniable, Briski underscored the need for caution. He warns that AI is not inherently transparent and trustworthy and emphasises the imperative of constructing robust foundations for quality assurance.

DEPT® employs automated testing to ensure responses align with expectations. Briski also stressed the importance of setting stringent parameters to guide AI conversations, ensuring alignment with the company’s ethos and desired consumer interactions.

“It’s important to really keep focused on the exact conversation you want to have with your consumer or your customer and put really strict guardrails around how you would like the model to answer those questions,” explains Briski.

In December, DEPT® is sponsoring AI & Big Data Expo Global and will be in attendance to share its unique insights. Briski is a speaker at the event and will be providing a deep dive into business intelligence (BI), illuminating strategies to enhance responsiveness through large language models.

“I’ll be diving into how we can transform BI to be much more responsive to the business, especially with the help of large language models,” says Briski.

As DEPT® continues to redefine digital paradigms, we look forward to observing how the company’s innovations deliver a new era in AI-powered experiences.

DEPT® is a key sponsor of this year’s AI & Big Data Expo Global on 30 Nov – 1 Dec 2023. Swing by DEPT®’s booth to hear more about AI and LLMs from the company’s experts and watch Briski’s day one presentation.

The post Bob Briski, DEPT®:  A dive into the future of AI-powered experiences appeared first on AI News.

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.

The post Basil Faruqui, BMC: Why DataOps needs orchestration to make it work appeared first on AI News.

The new offering targets data practitioners and non-technical users such as in marketing, sales, and revenue operations teams to build data pipelines within minutes. For example, it provides operations teams with a fast, free, and frictionless way to load, integrate and analyze high-quality campaign, pipeline, forecast, and revenue data. In addition, data analysts and data engineers benefit from increased productivity and rapid development. 

This is the second in a series of releases that began with the Informatica Data Loader launch in May 2022. Taken together, Informatica Data Loader, Cloud Data Integration-Free (CDI-Free), and PayGo (CDI-PayGo) are the industry’s only free data loading and integration solutions. They are natively built in to provide intelligent cloud data management services for all your data-driven use cases. Informatica CDI-Free, CDI-PayGo and Data Loader support all major data warehouses/lake solutions, including Amazon Redshift, Azure Synapse, Databricks Delta Lake, Google BigQuery, and Snowflake.  

Jitesh Ghai, chief product officer at Informatica, said: “We are redefining the data integration market by making it free, easy to use and accessible to everyone. Organisations face the challenge of ingesting huge volumes of data from disparate sources and then making sense of that information. There is a clear need for no setup and no code SaaS data integration tools that are free and pay-as-you-go to quickly get started serving both business-focused data engineers and non-technical business users and analysts.

“By giving business and non-technical users access to simple, cost-optimised data integration solutions, organisations can bring the power of data to the masses.”   

The key to a truly data-driven business is providing self-service data integration to users across the organisation in technical and business roles. Informatica CDI-Free and PayGo provide just that: 

• CDI-Free: A free service that allows users to process up to 20M rows for ELT or reach 10 processing hours for ETL, per month, whichever comes first. 
• CDI-PayGo: All the capabilities of CDI-Free with no limit on processing rows or hours of usage. CDI-PayGo comes with essential customer support and SOC2 compliance. In addition, users only pay for what they use with a credit card.  

Users benefit from easy setup, and usage of these data integration services with AI-powered automationnon – need for coding, setup, or any DevOps. In addition, the cloud data loading and integration ETL/ELT services can be easily accessed from each of Informatica’s ecosystem partners including Amazon Web Services, Databricks, Google Cloud, Microsoft Azure and Snowflake.  

Chris Eldredge, VP of data office at Paycor, said: “The ability to harness the power of data is a valuable competitive advantage. Having the right data integration platform enables a data foundation that drives agility, insights, and innovation for superior business results. The new Cloud Data Integration (CDI)-Free and PayGo products lower the barriers to get started with data integration.  These new products will open the door for more data professionals, including tech-savvy business users, to leverage best-in-class data integration tools from Informatica.” 

Matt Wienke, CEO of Infoverity, said: “Cloud Data Integration (CDI)-Free and PayGo are launchpads that will improve and serve those entering the data integration domain. The tools are intuitive to use and easy to navigate. CDI-Free will empower tech-savvy business users to begin moving their data to the cloud without committing to software costs. Furthermore, the option to scale up to Informatica’s enterprise-grade cloud platform minimises risks from the trial and adoption of these products.” 

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 Informatica launches AI tool for marketers  appeared first on AI News.

Perhaps no other industry has been as affected by advanced technologies and digital transformation as retail. As a result, the retail sector has experienced a major transition in the past couple of years, with customer experience becoming one of the most crucial brand differentiators.

Next-Gen POS Solutions Transform Retail Operations

AI-enabled advanced point of sale solutions has brought monumental changes in the retail industry—from the numerous applications in data management and computer vision to the use of machine learning for efficient inventory management.

Despite a substantial rise in the inclination toward online shopping, customers still prefer to shop from physical stores. According to a recent study, nearly 59% of consumers prefer visiting the store before purchasing, while 81% of the people planning to purchase premium-priced products like to check them in person first. This demonstrates that the retail experience is still important to consumers.

With the growing significance of enhancing customer experience, more and more retailers are emphasizing on improving the overall in-store experience. By incorporating AI into POS systems, retailers can collect and analyze massive amounts of customer data to gain valuable insights. AI can be used to integrate automation, allowing POS to process data from various touch points in real-time. Here are some additional features that retailers can benefit from, as follows:

• Unlock the Hidden Value of Data
• Gather Valuable Customer Insights
• Foster Delightful Customer Experiences
• Create Secure Payment Ecosystems
• Stimulate Intelligent In-store Product Placement
• Promote In-store Fraud Detection & Prevention

How Does the Future Look?

There is no doubt that AI has a lot to promise and deliver to the retail sector. AI can take things to the next level, especially in the POS domain, on account of its ability to capture, assess, and deliver valuable facts and statistics that will improve in-store engagement, enhance operations, and positively impact the bottom line.

Hence, numerous POS solution developers and providers are aiming at investing in AI to upgrade their current POS solutions. However, it remains to be seen how AI technology will affect the retail ecosystem as we know it.

“Editor’s note: This article is in association with The POS Report

The post Artificial Intelligence: Making Retail POS Systems Smarter appeared first on AI News.

Artists are increasingly using AI-powered tools for their creative projects. Alper Yesiltas, an Istanbul-based artist, has posted numerous images of celebrities that have been aged convincingly using AI.

One of his collections is called ‘Young Age(d)’ and uses AI to age well-known (and very much still alive) figures such as Greta Thunberg:

View this post on Instagram

A post shared by Alper Yesiltas (@alperyesiltas)

And the more divisive individual, Justin Bieber:

View this post on Instagram

A post shared by Alper Yesiltas (@alperyesiltas)

However, Yesiltas’ arguably more interesting collection is ‘As If Nothing Happened’ which imagines what deceased celebrities would look like.

The collection includes Freddie Mercury:

View this post on Instagram

A post shared by Alper Yesiltas (@alperyesiltas)

Steve Jobs:

View this post on Instagram

A post shared by Alper Yesiltas (@alperyesiltas)

And the beloved Princess Diana:

View this post on Instagram

A post shared by Alper Yesiltas (@alperyesiltas)

In 2012, a performance at Coachella made headlines after a “hologram” of deceased rapper Tupac Shakur joined Dr Dre and Snoop Dogg onstage. The hologram was created by the special effects production house Digital Domain and portrayed Shakur in his prime.

Thanks to Yesiltas, we can see what Shakur would likely look like today:

View this post on Instagram

A post shared by Alper Yesiltas (@alperyesiltas)

With permission from the artist while they’re alive, combining AI for convincing ageing with holographic technologies could be a way for performers that died early to preserve their legacies and continue delighting old – and even new – fans long after they’re gone.

Yesiltas’ work can be viewed in-person as part of the ‘Digital Serendipity’ exhibition at Akbank Sanat until 11 February 2023.

(Photo by James Kovin 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.

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

The post AI project imagines what deceased celebs would look like today appeared first on AI News.

Matchday may be the ‘pinnacle’ for sports fans, but for sports clubs, the real battleground is that period between the ‘live-action’ and the ‘actual creation’ of a deep and enriching relationship with their fan base. As competition is heating up to win the hearts and minds of fans through relevant marketing and compete through the ‘noise’, the pressure is on sports clubs and associations to become more innovative. 

But despite the vast data sets at the fingertips of sports marketers, there is much room for improvement when it comes to delivering relevant, personalised communications or messages, experiences, or content to fans in real-time.

Creating a relationship beyond matchday 

To create a relationship that goes beyond game day, sports brands must connect with fans on the right channels at the right time. With zero-party data, or data willingly shared by fans, it’s possible to know what makes fans tick as well as the best ways to engage with them.

How do sports clubs encourage fans to share more of their personal information? You know, the “good stuff” that goes beyond names and email addresses to who they’re attending matches with and if they also watch the game at home, for example. It’s all about the value exchange. And the value exchange begins with data. 

Revolutionising engagement with data 

Data allows sports clubs to move to a more enriched understanding of who their fans are. It gives them insight into their motivations and preferences. The biggest success of the sports clubs we work with is, with Cheetah experiences, fans willingly share their information.

To improve every fan’s experience along their digital journey, it’s vital that the communications they receive from the club are tailored. They have to be personalised to their particular wants and desires. That’s where the data comes in. While content is perhaps the “shiniest” element of the marketing mix; it’s the data and the insights that really make a difference. These elements provide clubs with all the information they need to create bespoke communications, helping to foster that one-to-one relationship with fans.  

Data is also key in creating effective partnerships with brands that want to sponsor sports clubs. Once clubs know more about the fans, their behaviours, and motivations at a country-level, the value of sponsorships can be greatly enriched. That’s because partners are looking for clubs with an engaged fan base, and the only way to get an engaged fan base is to know and create meaningful relationships with them. This in turn, allows clubs to have successful commercial partnerships, which drives revenue into the club – revenue that allows them to invest back into the team and secure top-end spots in competition.

Turning challenges into opportunities

Not too long ago, the customer experience began and ended on matchday. Today, however, that’s simply not the case. In this new digital era, passionate fans are engaging with clubs on different platforms, 24/7. There’s no winter break, pre-season, or rest days for fan engagement – it’s constantly game-on.

Even when the pandemic toppled the athletic landscape, seeing sports ground to a halt with no indication of coming back again; it wasn’t the time to stop engaging fans. Instead, it was more vital than ever to keep their passion alive. Developing new ways to build off of a captive audience who was still hungry for sports was the first order of business for sports clubs and absolutely key to their survival.

But first, these clubs had to turn their unknown audience into a known audience. Digital channels and engagement are vital to helping clubs connect with their fans. It allows them to achieve deep, long-lasting, and meaningful relationships. Once fans feel connected to their clubs, their love grows and that creates a foundation that supports revenue creation and successful commercial partnerships.

However, this is nearly impossible to do without insights from data. Many clubs still have their data in silos where the ticketing team only sees their data, the hospitality team only sees their data, and so on. Getting away from silos and gaining a unified understanding of fans – who they are, what life stage they’re in, and what they want from the club – from top to bottom throughout the organisation is vital to revolutionising engagement.

Take a look at the Barcelona Spotify deal. If Barcelona truly knew its fans better, the deal could have been worth a lot more. However, since they didn’t, they were only able to target about 1% of their fan base — the rest were essentially invisible to them. 

The key takeaway from Barcelona’s unfortunate situation is just how crucial it is to get your fans to share information and permissions with you willingly. It’s absolutely essential in marketing to them more effectively.

And, of course, we can’t talk about effective marketing in today’s world without bringing up the death of the cookie. Never has there been a greater need to get fans to share their personal and preference data willingly than now. Unfortunately, it’s not an “ask and you shall receive” kind of arrangement. Fans are increasingly weary when it comes to handing over their personal information. That’s why sports clubs need to offer an enticing value exchange.

Leverage data for a game-winning loyalty strategy

When it comes to the value exchange, savvy sports clubs know that it doesn’t always have to be a discount or a red-letter prize that entices fans to share their details. Access to exclusive content and community initiatives can also be the catalyst for zero-party data collection.

According to Cheetah Digital’s report for sports teams and associations, 55% of fans will share psychographic data points like purchase motivations and product feedback with sports brands. Even more, half of all fans surveyed said they desire incentives like coupons, loyalty points, or exclusive access in return for their data. 

With Cheetah Digital’s Customer Engagement Suite, there’s an entire platform that makes it easy to build the most relevant, integrated, and profitable customer experiences. Take a look:

• Cheetah Engagement Data Platform: This foundational data layer and personalisation engine enables marketers to drive data from intelligent insights to action at speed and scale.

• Cheetah Experiences: Interactive digital acquisition experiences are delivered to delight customers, collect first- and zero-party data, and secure valuable permissions needed to execute compliant and successful marketing campaigns.

• Cheetah Messaging: Enables marketers to create and deliver relevant, personalised marketing campaigns across all channels and touchpoints.

• Cheetah Loyalty: Provides marketers with the tools to create and deliver unique loyalty programs that generate an emotional connection between brands and their customers.

• Cheetah Personalisation: Enables marketers to leverage the power of machine learning and automated journeys to connect with customers on a one-to-one basis.

Acquisition helps to turn an “unknown” audience into a “known” audience. Why is this important? Well, with “known” fans come a lot of potential in the form of direct revenue, partner revenue, and participation.

The sports clubs to watch

Cheetah Digital has partnered with some of the world’s top sports brands and organisations to create and launch an array of successful campaign experiences with ease. Whether to boost match-day excitement, connect with fans, monetise a global audience, or increase content relevancy to reach a specific demographic; sports organisations are using Cheetah Experiences to create impactful digital experiences that drive results.

Below, we look at how Arsenal Football Club (F.C.) and the FA are leveraging a fully-fledged, zero-party data strategy to connect with fans on every digital channel and collect the preference insights and permissions required to drive personalisation initiatives. 

Arsenal F.C.

Arsenal F.C. intelligently uses data to enhance digital engagement amongst one of the largest and most passionate fan bases in the world – it’s estimated to be upwards of 750 million people! The club built out its omnichannel campaign strategies through various technologies with Cheetah Digital being the main platform. That ensures the communication it sends out is relevant to fans and that it’s communicated on the right platforms at the right times in the right tone. 

Adam Rutzler, Senior Campaign and Insight Manager at Arsenal, says the most crucial aspect of his team’s work is ensuring that fans receive the best content that’s most relevant to them. “We work with a magic triangle, the power of three – transactional data, a demographic segmentation, persona-led approach, and behavioural data,” he explains.

“We get a solid understanding of our fans by taking the combination of these three things and hitting the sweet spot in the middle. What are our fans buying, who are they, and how do they engage with our football club – that’s when we really get the power of understanding our fans, what they want from us, and how we can best give that to them.”

For example, Arsenal has found the score predictor game is well received with fans. It encourages them to guess the score of the upcoming match to win a prize. And that prize can be anything from signed shirts to training kits — whatever fans would desire. 

Where Arsenal has noticed the most traction and where it’s getting some real buy-in from fans, however, is in giving away those money-can’t-buy prizes, such as corner flags from matches. Fans are really excited about these types of prizes. That memorabilia from clubs is truly meaningful to fans who are very passionate about their teams.

Therefore, the experiences that we’re offering and serving up on behalf of the clubs that we work with need to be in tune with fans. They need to offer something fun and something that’s on-brand.

Going forward, Adam says he’s excited about all the possibilities data opens up for the club. “What’s exciting about the insights we’re working with right now to continue understanding our global following is the possibility of turning our triangle into a square by adding psychographic data in.

“We want to understand the fans’ attitudes, aspirations, and personalities. That will allow us to find out what motivates them to engage with certain communications of ours. If we understand that, it would provide us with some very powerful insights,” he says.

The Football Association (FA)

The FA has a grand ambition to double its contactable CRM database by 2024. Achieving this will drive direct revenue, boosting sales for the FA directly. It will increase partner revenue, expanding their reach and resonance with partners. And it will also drive participation in the sport at a grassroots level, which is basically the cornerstone of what the FA does.

In terms of value exchange, the club is achieving above-average conversion rates, using a diverse set of tools like team sectors, man-of-the-match polls, and score predictors for upcoming FA Cup competitions. According to Paul Brierley, CRM & Membership Lead at the FA, the reason the FA’s strategy has been so effective boils down to its value proposition and relevance.

“Cheetah experiences, in particular, are helping us to drive an incredibly effective value exchange with fans. The combination of sought-after prizes, relevance and timing of that prize, and a compelling gamification experience is producing a highly successful channel for fan experience and data growth,” he says.

Future success

Going forward, there’s no other way for a sports club to be successful without understanding its fan base. It’s paramount to capture their motivations, intentions, and preferences at scale to provide a truly personalised experience. By leveraging Cheetah Experiences and offering a value exchange, fans will tell all – the products they desire, what they look for in a loyalty program, and what motivates them to engage. And that information translates to a hugely successful club both now and into the future.

Download this campaign guide packed with examples from leading sports brands and associations that are delivering engaging, interactive experiences in return for fans’ opt-ins and preference data, and then using this data to deliver true personalisation.

(Editor’s note: This article is in association with Cheetah Digital)

The post How sports clubs achieve a slam dunk in loyalty with data appeared first on AI News.

Deepfakes use generative neural network architectures –  such as autoencoders or generative adversarial networks (GANs) – to manipulate or generate visual and audio content.

Lamar is widely considered one of the greatest rappers of all time. However, he’s regularly proved his creative mind isn’t limited to his rapping talent.

For his track ‘The Heart Part 5’, Lamar has made use of deepfake technology to seamlessly morph his face into various celebrities including Kanye West, Nipsey Hussle, Will Smith, and even O.J. Simpson.

You can view the music video below:

For due credit, the deepfake element was created by a studio called Deep Voodoo.

Deepfakes are often used for entertainment purposes, including for films and satire. However, they’re also being used for nefarious purposes like the creation of ‘deep porn’ videos without the consent of those portrayed.

The ability to deceive has experts concerned about the social implications. Deepfakes could be used for fraud, misinformation, influencing public opinion, and interfering in democratic processes.

In March, a deepfake purportedly showing Ukrainian President Volodymyr Zelenskyy asking troops to lay down their arms in their fight to defend their homeland from Russia’s invasion was posted to a hacked news website.

“I only advise that the troops of the Russian Federation lay down their arms and return home,” Zelenskyy said in an official video to refute the fake. “We are at home and defending Ukraine.”

Fortunately, the deepfake was of very low quality by today’s standards. The fake Zelenskyy had a comically large and noticeably pixelated head compared to the rest of his body. The video probably didn’t fool anyone, but it could have had major consequences if people did believe it.

One Russia-linked influence campaign – removed by Facebook and Twitter in March – used AI-generated faces for a fake “editor-in-chief” and “columnist” for a linked propaganda website.

The more deepfakes that are exposed will increase public awareness. Artists like Kendrick Lamar using them for entertainment purposes will also help to spread awareness that you can no longer necessarily believe what you can see with your own eyes.

Related: Humans struggle to distinguish between real and AI-generated faces

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 Kendrick Lamar uses deepfakes in latest music video appeared first on AI News.

Fans know there is a factory of high-end engineers who craft the cars that do battle on tracks globally, but there is another equally important and high-end team that is less visible – data.

The Bahrain Grand Prix, for example, demonstrated the power of data to win a race. In one of the closest and hardest-fought contests, Lewis Hamilton and the Mercedes-AMG team beat hard competition from Red Bull and Max Verstappen, who had the lead from the start lights.

While Hamilton and Mercedes stayed close to Red Bull, thanks to the team’s data-decision strategies, Mercedes were able to execute an undercut, a decision to pit early for fresh tyres and use the extra performance from those tyres to take the lead.

The fresh tyres meant Hamilton was able to lap the circuit up to two seconds faster than Verstappen.

The undercut took place on the first pit stop, and the plan was to put Verstappen under pressure of a second undercut from third-placed Valtteri Bottas in the second Mercedes.

However, a mechanical issue delayed Bottas’ pit stop, and leader Hamilton faced the possibility that Verstappen and Red Bull would counterattack with their own undercut.

Bahrain is a race that has high tyre wear, and since Hamilton pitted early he had to do extra laps on worn tyres. This allowed Verstappen to get close to Hamilton and put the team under immense pressure in the closing laps.

Hamilton won by just half a second, with driver excellence in protecting tyres – combined with the team’s data-decision strategies – carrying the victory.

The timing of pit stops to execute an undercut is just one area where data has changed the race.

For the 2021 season, new rules were introduced related to car aerodynamics. A new aerodynamic package can completely change the characteristics of the car.

Mercedes-AMG uses TIBCO Spotfire to keep track of the car set-ups used by the team across the season, and to unpick that data and map it to new data from testing and simulations.

Together, these data sets provide insights into how the car behaves under the new regulations, and this helps direct car development and race strategies.

Spotfire is a key tool in post-race reviews, allowing the team to analyse race events such as race starts, and develop data sets focused on a track and its conditions, which are invaluable for future races.

Insights the team has gained include braking traction, tyre traction recovery, and throttle usage, all of which are used to understand and tweak ongoing race strategies.

Digital twin to the test

The data collected from each race is used in the build up to the next race. The team developed a digital twin of the car, including mathematical models of the car’s sub-assemblies.

This enables the team to test and analyse millions of car set-up and race scenarios prior to upcoming events, without a real car turning a wheel. Simulations are run for more than 50 set-up parameters in the sub-assemblies, as well as considerations for elements such as the weather conditions and driver preferences.

The digital twin also enables Mercedes-AMG’s team to constantly tweak the car, with different teams of experts working on different modules and sub-assemblies.

Visual analysis capabilities enable the vehicle dynamics teams to share their insights with the track engineers, who can then drill down, filter and run what-if scenarios and trade-offs to identify areas of performance advantage. 

Collaboration is key, and engineers at the factory, or trackside engineers travelling from race to race, share information and prepare for the Grand Prix ahead.

Engineering teams often come together en route to the track, at an airport or even in the plane to look at the simulation data and discover opportunities for performance improvement.

Once at the racetrack, the collaboration continues, and the simulation data feeds into setting the car up for the practice sessions, qualifying and race day.

As the practice sessions unfold, the trackside team responds to changes in weather and incorporates feedback from the drivers.

This same level of analysis is used to optimise the efforts in developing and manufacturing the car.

A new cost cap was recently introduced to the sport by the FIA, the governing body of motor racing. This keeps annual spending at $145 million per team, and while this is a significant amount of money, this budget has to cover the design, engineering, operating, and racing costs for the entire Mercedes-AMG Petronas Formula One organisation.

When the car build and racing costs are taken out of the $145 million, the rest of the development budget is quite constrained.

So, any savings Mercedes-AMG can achieve boosts the development budget available to keep the car at the front of the grid.

The Mercedes-AMG and the TIBCO Data Science teams collaborated to develop Spotfire visual analytics tools that provide up-to-the-minute cost and value information to engineers and business staff.

The Spotfire analysis features a tree map of car components with drill-down to supplementary tabular data that quantify the value and cost of components.

This enables individual teams and engineers to work in parallel, optimising their sub-assembly value and cost.

The TIBCO Data Science team also developed a custom “concertina” visualisation, using ”Spotfire Mods“, to analyse cost and value holistically.

In this visual analysis, all the car sub-assemblies are included and shown at a high-level, with drill down into each fold of the concertina to analyse individual value cost trade-offs within and among sub-assemblies.

These visual analyses have resulted in some quick wins, including selective use of protective coatings on car parts and their surfaces. The use of such coatings was previously widespread, and the teams are now able to trim costs with more judicious use of some higher cost coatings.

Bottom line, at every twist and turn of every race, across the season and at every decision, the drivers, team managers and engineers make data and analytics play a central role.

From car design and manufacturing to car setup, configuration, and race strategy, the Mercedes-AMG team stay at the top of the analytics heap. The past seven consecutive driver and constructor championships are a clear testament to this!

The post Data analytics’ centrality to F1 racing appeared first on AI News.

Delivering this without artificial intelligence (AI) would be comparable to traversing the Amazon rainforest armed with nothing but a spoon.

To cut – or scoop – through this jungle of music, Spotify’s research team deploy hundreds of machine learning models that improve the user experience, all the while trying to balance the needs of users and creators.

AI News caught up with Spotify research lead Rishabh Mehrotra at the AI & Big Data Expo Global on September 7 to learn more about how AI supports the platform.

AI News: How important is AI to Spotify’s mission?

Rishabh Mehrotra: AI is at the centre of what we do. Machine learning (ML) specifically has become an indispensable tool for powering personalised music and podcast recommendations to more than 365 million users across the world. It enables us to understand user needs and intents, which then helps us to deliver personalised recommendations across various touch points on the app.

It’s not just about the actual models which we deploy in front of users but also the various AI techniques we use to adopt a data driven process around experimentation, metrics, and product decisions.

We use a broad range of AI methods to understand our listeners, creators, and content. Some of our core ML research areas include understanding user needs and intents, matching content and listeners, balancing user and creator needs, using natural language understanding and multimedia information retrieval methods, and developing models that optimise long term rewards and recommendations.

What’s more, our models power experiences across around 180 countries, so we have to consider how they are performing across markets. Striking a balance between pushing global music but still facilitating local musicians and music culture is one of our most important AI initiatives.

AN: Spotify users might be surprised to learn just how central AI is to almost every aspect of the platform’s offering. It’s so seamless that I suspect most people don’t even realise it’s there. How crucial is AI to the user experience on Spotify?

RM: If you look at Spotify as a user then you typically view it as an app which gives you the content that you’re looking for. However, if you really zoom in you see that each of these different recommendation tools are all different machine learning products. So if you look at the homepage, we have to understand user intent in a far more subtle way than we would with a search query. The homepage is about giving recommendations based on a user’s current needs and context, which is very different from a search query where users are explicitly asking for something. Even in search, users can seek open and non-focused queries like ‘relaxing music’, or you could be searching the name of a specific song.

Looking at sequential radio sessions, our models try to balance familiar music with discovery content, aimed at not only recommending content users could enjoy at the moment, but optimising for long term listener-artist connections.

A good amount of our ML models are starting to become multi-objective. Over the past two years, we have deployed a lot of models that try to fulfil listener needs whilst also enabling creators to connect with and grow their audiences.

AN: Are artists’ wants and needs a big consideration for Spotify or is the focus primarily on the user experience?

RM: Our goal is to match the creators with the fans in an enriching way. While understanding user preferences is key to the success of our recommendation models, it really is a two-sided market in a lot of ways. We have the users who want to consume audio content on one side and the creators looking to grow their audiences on the other. Thus a lot of our recommendation products have a multi-stakeholder thinking baked into them to balance objectives from both sides.

AN: Apart from music recommendations and suggestions, does AI support Spotify in any other ways?

RM: AI plays an important role in driving our algotorial approach – Expert curators with an excellent sense for what’s up and coming, quite literally teach our machine learning system. Through this approach, we can create playlists that not only look at past data but also reflect cultural trends as they’re happening. Across all regions, we have editors who bring in deep domain expertise about music culture that we use proactively in our products. This allows us to develop and deploy human-in-the-loop AI techniques that can leverage editorial input to bootstrap various decisions that various ML models can then scale.

AN: What about podcasts? Do you utilise AI differently when applying it to podcasts over music?

RM: Users’ podcast journeys can differ in a lot of ways compared to music. While music is a lot about the audio and acoustic properties of songs, podcasts depend on a whole different set of parameters. For one, it’s much more about content understanding – understanding speakers, types of conversations and topics of discussions.

That said, we are seeing some very interesting results using music taste for podcast recommendations too. Members of our group have recently published work that shows how our ML models can leverage users’ music preferences to recommend podcasts, and some of these results have demonstrated significant improvements, especially for new podcast users.

AN: With so many models already turning the cogs at Spotify, it’s difficult to see how new and exciting use cases could be introduced. What are Spotify’s AI plans for the coming years?

RM: We’re working on a number of ways to elevate the experience even further. Reinforcement learning will be an important focus point as we look into ways to optimise for a lifetime of fulfilling content, rather than optimise for the next stream. In a sense this isn’t about giving users what they want right now as opposed to evolving their tastes and looking at their long term trajectories.

AN: As the years go on and your models have more and more data to work with, will the AI you use naturally become more advanced?

RM: A lot of our ML investments are not only about incorporating state-of-the-art ML into our products, but also extending the state-of-the-art based on the unique challenges we face as an audio platform. We are developing advanced causal inference techniques to understand the long term impact of our algorithmic decisions. We are innovating in the multi-objective ML modelling space to balance various objectives as part of our two-sided marketplace efforts. We are gravitating towards learning from long term trajectories and optimising for long term rewards.

To make data-driven decisions across all such initiatives, we rely heavily on solid scientific experimentation techniques, which also heavily relies on using machine learning.

Reinforcement learning furthers the scope of longer term decisions – it brings that long term perspective into our recommendations. So a quick example would be facilitating discovery on the platform. As a marketplace platform, we want users to not only consume familiar music but to also discover new music, leveraging the value of recommendations. There are 70 million tracks on the platform and only a few thousand will be familiar to any given user, putting aside the fact that it would take an individual several lifetimes to actually go through all this content. So tapping into that remaining 69.9 million and surfacing content users would love to discover is a key long-term goal for us.

How to fulfil users’ long term discovery needs, when to surface such discovery content, and by how much, not only across which set of users, but also across various recommended sets are a few examples of higher abstraction long term problems that RL approaches allow us to tackle well.

AN: Finally, considering the involvement Spotify has in directing users’ musical experiences, does the company have to factor in any ethical issues surrounding its usage of AI?

RM: Algorithmic responsibility and causal influence are topics we take very seriously and we actively work to ensure our systems operate in a fair and responsible manner, backed by focused research and internal education to prevent unintended biases.

We have a team dedicated to ensuring we approach these topics with the right research-informed rigour and we also share our learnings with the research community.

AN: Is there anything else you would like to share?

RM: On a closing note, one thing I love about Spotify is that we are very open with the wider industry and research community about the advances we are making with AI and machine learning. We actively publish at top tier venues, give tutorials, and we have released a number of large datasets to facilitate academic research on audio recommendations.

For anyone who is interested in learning more about this I would recommend checking out our Spotify Research website which discusses our papers, blogs, and datasets in greater detail.

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

The post Rishabh Mehrotra, research lead, Spotify: Multi-stakeholder thinking with AI appeared first on AI News.

70 percent of the 2,341 respondents across 30 business sectors picked AR as disrupting their industry most out of a selection of seven emerging technologies: AI, cybersecurity, cloud computing, IoT, blockchain, and 5G.

Filipe Oliveira, Senior Analyst at GlobalData, commented: “This change in how people see AR will likely be long term, and not just a temporary blip. It is clear that people are warming towards the technology, even if they don’t believe that it will make a big difference tomorrow.” 

AI wins some ground back when it comes to confidence in the technology. 57 percent of the respondents believe that AI will live up to all of its promises compared to just 26 percent for AR.

Along those same lines, 31 percent believe “The technology is hyped, but I can see a use for it” for AI, while a huge 50 percent report the same for AR.

Apple’s decision to add a LiDAR sensor to its latest mobile devices was seen as an important step towards the mass adoption of AR. Excitement is also growing around so-called “metaverses” that converge virtually-enhanced physical reality with physically-persistent shared virtual spaces.

SenseTime, one of China’s leading AI companies, announced earlier this week that it had partnered with BilibiliWorld to create a metaverse. The experience leverages SenseTime’s AI and mixed reality technologies to enable players to enjoy role-playing games that seamlessly blend reality with virtuality.

Facebook CEO Mark Zuckerberg recently said the company “will effectively transition from people seeing us as primarily being a social media company to being a metaverse company”. As the owner of Oculus, Zuckerberg’s plans for the future of Facebook will likely make people think of a large virtual space similar to that depicted in Ernest Cline’s Ready Player One novel and the 2018 film adaptation.

Some people have expressed concern about a large centralised company such as Facebook having control over such a potentially ubiquitous world and the content they consume. Many believe that an open-source decentralised version is vital:

Zuckerberg, for his part, claims that no one company will run the metaverse and it will be an “embodied internet” that is operated by many different players.

Decentraland is an early example of what a truly decentralised virtual space could look like. The platform makes use of a DAO (Decentralised Autonomous Organisation) to make policy decisions such as what content is allowed in addition to taking advantage of the NFT (Non-Fungible Token) trend to offer exclusive in-world items.

AR and AI are both important emerging technologies that can often go hand-in-hand, but it’s clear that the latter is losing its perspective among professionals as having the biggest impact on their industries over the coming years.

(Photo by My name is Yanick on Unsplash)

Want to find out more from executives and thought leaders in this space? Find out more about the Digital Twin World event, taking place on 8-9 September 2021, which will explore augmenting business outcomes in more depth and the industries that will benefit.

The post AR overtakes AI as the ‘most disruptive’ emerging technology appeared first on AI News.