AI is fundamentally reshaping customer engagement and personalisation by enabling businesses to deliver tailored experiences and responses to individual customer preferences on a large scale.

During our conversation, we discussed how Twilio is at the forefront of this revolution as well as addressing the stark contrast between companies’ claims of personalisation and customers’ actual experiences.

AI News: How are you using AI to deliver more personalised and satisfactory customer experiences? 

Hema Thanki: According to Twilio’s recent State of Customer Engagement report, 91 percent of companies say they always or often personalise engagement with consumers. But, consumers don’t agree. Only 56 percent of consumers report that their interactions with brands are always or often personalised. 

Instead of becoming “customer-centric,” most companies have become “system-centric.” Exploding tech stacks and patchwork solutions lead to fragmented data, an incomplete view of the customer and, ultimately, disjointed experiences.

The reality is that every consumer is a complex individual with unique wants and needs that change from moment to moment. In order to truly put customers at the heart of your business, you need to be able to know who your customers are, understand how to best meet their needs and exceed their expectations, and then activate those insights to engage them how, when, and where is most relevant and meaningful to them. 

These elements make up the engagement flywheel that is key to powering dynamic customer engagement that adapts to every individual customer at scale.

We recently announced Twilio CustomerAI to unlock the power of AI for hundreds of thousands of businesses and supercharge the engagement flywheel. With CustomerAI, brands can expand their perception of customer data, activate it more extensively, and be better informed by a deeper understanding of their customers. 

AN: What are some examples of how businesses can use Twilio CustomerAI? 

HT: Today’s marketers need to not only understand past customer behaviour but must be able to anticipate and act on customers’ future wants and needs. AI and machine learning (ML) models are incredibly effective at doing this but are complex to build and require data science expertise.

With CustomerAI Predictions now generally available, Twilio Segment is putting the power of predictive AI at marketers’ fingertips. Without having to tap technical teams, marketers can now create precisely targeted audiences out-of-the-box, trigger customer journeys, and personalise multichannel experiences based on a customer’s predicted lifetime value (LTV), likelihood to purchase or churn, or propensity to perform any other event tracked in Segment.

Brands like Box are using Predictions to save time, optimise campaign performance, and discover revenue opportunities:

“As marketers, the holy grail is to reach your customers and prospects in a way that is meaningful, relevant and additive to them. CustomerAI Predictions has equipped Box’s marketing team with the ability to forecast customer behaviour to a degree that was simply unavailable to us before.

We’ve been able to explore segmenting our audience based on predictive traits like who is most likely to join us at in-person events or who is more likely to purchase, and this allows us to meet those people where they are in their customer journey. Tools like Predictions put marketers at the centre of this new era of AI which is transforming how companies engage and retain their customers.” – Chris Koehler, CMO at Box.

AN: What other emerging AI trends should people be keeping an eye on? 

HT: When companies rely on managing data in a customer data platform (CDP) in tandem with AI, they can create strong, personalised campaigns that reach and inspire their customers. Here are four trends in AI personalisation.

1. Personalised product recommendations: Using AI to serve personalised product recommendations is a way to ensure your customers are being served optimized content. It can also build trust with your brand, leading to repeat purchases. Take Norrøna, an outdoor clothing brand in Scandinavia. They built a complete recommendation platform – from data collection to machine learning predictions, in just six months. Norrøna relied on Segment for the collection and management of client–side and server–side customer data. Segment assigned an ID to each customer and ensured the data collected on them was clean, thanks to the schema.
2. Behaviour–based email campaigns: AI is getting us as close as possible to identifying patterns in user interactions and helping with creating behaviour–based email campaigns. If a customer frequently clicks on one type of email content, an AI–powered system could trigger an email to that customer containing content related to what they are clicking. Using Twilio Engage, you can send emails right from the Segment app, relying on your first-party customer data to lead the way.
3. Dynamic website content: The days of static landing pages are in the past, thanks to tools that collect user behaviour and churn out personalised website content in real-time. In fact, Segment’s website displays dynamic content to customers based on their own interests—thanks to integration with Mutiny. Whenever a visitor lands on the Segment website from a particular IP address, they will be met with personalised landing pages based on their unique behaviour. This means personalised content for every single visitor.
4. Predictive customer segmentation: Predictive Audiences let businesses target users who have an increased likelihood of performing an event. It works with out-of-the-box audience templates that are pre-built with Predictions. They include templates like “ready to buy” or “potential VIPs.” AI can then analyse these profiles to create predictive segments. For instance, AI could identify a group of customers who are likely to churn based on their behaviour. This allows you to proactively engage with these customers through tailored retention campaigns.

AN: Do you have any best practices and tools that you use for testing, monitoring, and debugging your AI models and applications to ensure quality and reliability? 

HT: Customer data unlocks the promise of AI as a unique market advantage, but your AI is only as good as the data you put into it. If your data is siloed, stale, inconsistent, and incomplete, your AI outputs will reflect that. At Twilio Segment, we have a long history of helping companies build trusted data infrastructures with unified, real-time, consistent, and consented data that is critical to your AI strategy. 

Our composable CDP ensures your data is AI-ready, helping you collect, clean, and activate customer data with our open, API-first platform and 450+ pre-built connectors that enable you to start with data anywhere and activate it everywhere.

With Segment, you choose where you start. Whether that’s getting data from SaaS products into your data warehouse, or activating existing data with reverse ETL, Segment gives you the flexibility and extensibility to move fast, scale with ease, and efficiently achieve your business goals as they evolve.

AN: Are you seeing more customers looking for AI solutions to improve operational efficiencies amid global economic uncertainties? 

HT: Marketers spend massive amounts of time writing, designing, and building campaigns and customer journeys. With generative AI soon available (scheduled for public beta in 2024) inside Twilio Engage and Segment CDP, marketers can save precious time, boost productivity, and optimise for stronger results. 

Using the new CustomerAI Generative Email coming to Twilio Engage, marketers will be able to enter simple text prompts that turn ideas into HTML in minutes. This builds on the AI capabilities available in Twilio Engage today, such as our Smart Email Content Editor which suggests conversion-worthy email headlines, images, and calls-to-action to drive better engagement with the click of a button.

Meanwhile, marketers will be able to skip the manual process of architecting customer journeys thanks to CustomerAI Generative Journeys. Soon, they will be able to simply describe campaign type (promotional, win-back, etc.), audience definition, and which channels they want to use, then Twilio Engage will automatically build the journey using generative AI—saving marketers time while accelerating growth.

AN: What will Twilio be sharing with the audience at this year’s AI & Big Data Expo Europe?

HT: Twilio Segment is excited to be taking part in AI & Big Data Expo Europe in 2023! As a proud sponsor, we’ll have a strong exhibiting presence – feel free to drop by stand 242 to meet a member of the team and discuss how Segment can collect, unify and activate your customers’ first-party data.

We’ll be joined by Folkert Mudde from Albert Heijn who’ll be talking through their positive partnership highlighting how the supermarket chain supercharges personalisation for 4+ million recognised users with Twilio Segment. Check out this session on 26 September within the ‘Applied Digital, Data & AI’ track at 14:30 CET.

Arthur Viegers, SVP of Global Engineering at Twilio Segment, will be taking part in a dynamic panel discussion on 27 September at 11:40 CET on the topic of ‘The Future of AI-Enabled Experiences’.

Twilio Segment is a key sponsor of this year’s AI & Big Data Expo Europe on 26-27 September 2023. Swing by the company’s booth at stand #242 to hear more about AI from the company’s experts.

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One of the key challenges in developing and deploying deep learning models lies in their size and computational requirements. However, Neural Magic tackles this issue head-on through a concept called compound sparsity.

Compound sparsity combines techniques such as unstructured pruning, quantisation, and distillation to significantly reduce the size of neural networks while maintaining their accuracy. 

“We have developed our own sparsity-aware runtime that leverages CPU architecture to accelerate sparse models. This approach challenges the notion that GPUs are necessary for efficient deep learning,” explains Bogunowicz.

Bogunowicz emphasised the benefits of their approach, highlighting that more compact models lead to faster deployments and can be run on ubiquitous CPU-based machines. The ability to optimise and run specified networks efficiently without relying on specialised hardware is a game-changer for machine learning practitioners, empowering them to overcome the limitations and costs associated with GPU usage.

When asked about the suitability of sparse neural networks for enterprises, Bogunowicz explained that the vast majority of companies can benefit from using sparse models.

By removing up to 90 percent of parameters without impacting accuracy, enterprises can achieve more efficient deployments. While extremely critical domains like autonomous driving or autonomous aeroplanes may require maximum accuracy and minimal sparsity, the advantages of sparse models outweigh the limitations for the majority of businesses.

Looking ahead, Bogunowicz expressed his excitement about the future of large language models (LLMs) and their applications.

“I’m particularly excited about the future of large language models LLMs. Mark Zuckerberg discussed enabling AI agents, acting as personal assistants or salespeople, on platforms like WhatsApp,” says Bogunowicz.

One example that caught his attention was a chatbot used by Khan Academy—an AI tutor that guides students to solve problems by providing hints rather than revealing solutions outright. This application demonstrates the value that LLMs can bring to the education sector, facilitating the learning process while empowering students to develop problem-solving skills.

“Our research has shown that you can optimise LLMs efficiently for CPU deployment. We have published a research paper on SparseGPT that demonstrates the removal of around 100 billion parameters using one-shot pruning without compromising model quality,” explains Bogunowicz.

“This means there may not be a need for GPU clusters in the future of AI inference. Our goal is to soon provide open-source LLMs to the community and empower enterprises to have control over their products and models, rather than relying on big tech companies.”

As for Neural Magic’s future, Bogunowicz revealed two exciting developments they will be sharing at the upcoming AI & Big Data Expo Europe.

Firstly, they will showcase their support for running AI models on edge devices, specifically x86 and ARM architectures. This expands the possibilities for AI applications in various industries.

Secondly, they will unveil their model optimisation platform, Sparsify, which enables the seamless application of state-of-the-art pruning, quantisation, and distillation algorithms through a user-friendly web app and simple API calls. Sparsify aims to accelerate inference without sacrificing accuracy, providing enterprises with an elegant and intuitive solution.

Neural Magic’s commitment to democratising machine learning infrastructure by leveraging CPUs is impressive. Their focus on compound sparsity and their upcoming advancements in edge computing demonstrate their dedication to empowering businesses and researchers alike.

As we eagerly await the developments presented at AI & Big Data Expo Europe, it’s clear that Neural Magic is poised to make a significant impact in the field of deep learning.

You can watch our full interview with Bogunowicz below:

(Photo by Google DeepMind on Unsplash)

Neural Magic is a key sponsor of this year’s AI & Big Data Expo Europe, which is being held in Amsterdam between 26-27 September 2023.

Swing by Neural Magic’s booth at stand #178 to learn more about how the company enables organisations to use compute-heavy models in a cost-efficient and scalable way.

The post Damian Bogunowicz, Neural Magic: On revolutionising deep learning with CPUs appeared first on AI News.

Irakli started with a positive note that’s easily forgotten: never has the world been more safe, connected, and prosperous.

“We have developed technologies which have the potential to solve problems we never thought were possible,” says Beridze. “Most of them are related to the UN’s sustainable development goals.”

World-Changing Benefits

A look at the statistics provides evidence of a huge reduction in those dying from violence or living in extreme poverty. Many of the greatest threats we face today are shared challenges such as climate change, disease, and dwindling resources.

AI is a powerful tool which can help with all these challenges and more if we, as humankind, choose to use it this way. Alternatively, it could pose an existential threat.

Here are just some of the ways Beridze expects AI to aid the UN towards its goals:

Beridze dives deeper into some other potential benefits of AI to societies. A couple of the most interesting suggestions are its use to improve health and wellbeing, and to maintain peace, justice, and strong institutions.

Starting with health, Beridze highlights the use of AI to analyse large quantities of healthcare data in order to make scientific breakthroughs. Furthermore, it could be used to predict and project disease outbreaks to reduce mortality rates.

The impact of AI on healthcare is among our most covered subjects here on AI News. There are exciting developments on a near-daily basis.

Next up is the potential for AI when it comes to peace, law, and governance. Beridze believes AI can be integrated within an ‘e-government’ to reduce discrimination, prejudice, and corruption.

AI currently has a well-documented bias problem. However, solutions are becoming available to ensure the algorithms behind AIs are fair and do not favour any part of society over another. It’s ultimately easier to make a machine less discriminate than a person.

Global powers are seeking to establish themselves as leaders in AI. China and the US continue to be dominant by pumping billions of cash into developments, while smaller economies are playing to their own strengths.

Countries such as Japan are strong in fields such as robotics. The EU has the highest number of service robot manufacturing. Meanwhile, the UK is known for its leadership in ethics and strong academic attributes with leading universities.

There’s a now famous quote from Russian President Vladimir Putin speaking about AI which said: “Whoever becomes the leader in this sphere will become the ruler of the world.”

Putin’s quote was received in many ways. Some believe it was simply a matter of fact, while others saw it as confirmation of a potentially reckless race between world powers to become a leader.

AI-as-a-Threat

Regardless of what states do, criminals will seek to exploit AI for their own gain. This could take many forms, but one clear example is that of impersonation.

During Google’s I/O conference this year, the company showed off its Duplex demo where an AI assistant called a hair salon on a user’s behalf and was convincing enough to pass for a human. By training such a system with someone else’s voice, fraud could be completely automated.

Beridze will be meeting with Interpol next month to discuss the new risks posed by criminals using artificial intelligence, and how law enforcement agencies can work to counter them.

“When talking about the good sides of AI, we should never forget about the possible risks,” warns Beridze. “One of the biggest risks is the pace of development with how quickly it’s being developed and how fast we can adapt to that.”

One major concern is the potential impact on jobs. Low-wage workers are particularly threatened by automation.

“We don’t really have any solutions,” Beridze says. “We have some ideas that have been put on the table such as; Universal Basic Income, retraining of the population, some even say to slow down the pace of innovation.”

Other concerns highlighted by Beridze includes automated weapons, superintelligent systems like SkyNet famously depicted in the Terminator movies, and using things such as bots to influence democratic processes.

Solving International Verification

One of the most interesting uses for AI is for the verification of incidents where nations do not trust each other. This has perhaps been seen most often between Western nations and Russia where there’s still a clear level of distrust.

Take the recent chemical attack in Salisbury, UK on a former spy and his daughter. Western nations agreed it could only have been carried out by Russia. For its part, Russia denies the allegations and claims to have been locked out of seeing any evidence.

Beridze served as a special projects officer at the OPCW (Organisation for the Prohibition of Chemical Weapons) prior to joining UNICRI.

The OPCW is an independent organisation working alongside the United Nations that investigates chemical attacks. Members of OPCW represent around 98 percent of the global population.

Until a ruling last week, the OPCW was prohibited from assigning blame for a chemical attack. In Salisbury, the organisation stated it agreed with the UK’s findings the nerve agent of the kind first developed by the Soviet Union.

There have also been multiple chemical attacks in Syria. One particularly devastating attack in Douma was to be investigated by the OPCW but investigators claim they were blocked from accessing the site by Syria and its Russian allies.

Investigators were eventually provided access over a week after. However, Chlorine – at least one of the suspected chemicals used – is notoriously difficult to detect even a day after because of its gaseous state.

Russia and Syria both reject claims that chemical weapons were used. Moscow has offered several narratives on Douma, claiming simultaneously that there never was an attack and that it was the work of rebels in the area.

France said it was likely the evidence is gone, and the USA accused Russia and Syria of tampering with the site.

When everyone is pointing the finger at each other, there needs to be independent verification. Whenever people are involved there’s nearly always some accusations of foul play.

A provably unbiased, open-source AI which examines the evidence could be the answer.

“The time has come where we should employ technologies like AI and blockchain to start verification of issues where countries do not trust each other,” says Beridze. “We need to make a major leap from a system created in the [19]40s, to 80 years down the road where we live in a completely different world.”

Beridze’s session highlighted both the near-limitless potential for AI to have a positive impact on the world, or it could just as easily be devastating.

One thing is for sure, AI will transform our world. For better or worse, that’s up to all of us.

You can watch our interview with Irakli Beridze below:

Find out more about AI Expo and the next event here.

What impact do you think AI will have on the world? Let us know in the comments.

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

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