AI Booster is the result of 18 months of development and is built upon Google’s Vertex AI and integrates with Vodafone’s Neuron platform.

Vertex AI, among other Google technologies, had not been officially announced when Vodafone started development on AI Booster.

Cornelia Schaurecker, Global Group Director for Big Data & AI at Vodafone, said:

“To maximise business value at pace and scale, our vision was to enable fast creation and horizontal/vertical scaling of use cases in an automated, standardised manner. To do this, 18 months ago we set out to build a next-generation AI/ML platform based on new Google technology, some of which hadn’t even been announced yet.

We knew it wouldn’t be easy. People said, ‘Shoot for the stars and you might get off the ground…’ Today, we’re really proud that AI Booster is truly taking off, and went live in almost double the markets we had originally planned.

Together, we’ve used the best possible ML Ops tools and created Vodafone’s AI Booster Platform to make data scientists’ lives easier, maximise value, and take co-creation and scaling of use cases globally to another level.”

Google’s Vertex AI lets customers build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified platform.

Ashish Vijayvargia, Analytics Product Lead at Vodafone, commented:

“As a technology platform, we’re incredibly proud of building a cutting-edge MLOps platform based on best-in-class Google Cloud architecture with in-built automation, scalability, and security.

The result is we’re delivering more value from data science while embedding reliability engineering principles throughout.”

Vodafone highlighted four key features of AI Booster:

1. Automated ML lifecycle compliance activities (drift/skew detection, explainability, auditability, etc.) via reusable pipelines, containers, and managed services.
2. Embedded security by design.
3. Capitalise on Google-native ML tooling using BQML, AutoML, Vertex AI and others.
4. Boost adoption through standardised and embedded ML templates.

The new platform reportedly enables Vodafone’s data scientists and developers to slash the time of going from proof-of-concept to production from five months to just four weeks.

Vodafone and Google have forged a very close relationship in recent years. In May 2021, the two companies extended their relationship to build a global data platform.

“Vodafone’s flourishing relationship with Google Cloud is a vital aspect of our evolution toward becoming a world-leading tech communications company,” said Cengiz Ucbenli, Global Head of Big Data and AI, Innovation, Governance at Vodafone.

“It accelerates our ability to create faster, more scalable solutions to business challenges like improving customer loyalty and enhancing customer experience, whilst keeping Vodafone at the forefront of AI and data science.”

(Image Credit: Vodafone)

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London-based Babylon Health is a digital-first health service provider that uses AI and machine learning technology to provide access to health information to people whenever and wherever they need it.

The company has partnered with private and public across the UK, North America, South-East Asia, and Rwanda with the aim of making healthcare more accessible and affordable to 24 million patients worldwide.

“Our job is to help people to stay well and we’re on a mission to provide affordable, accessible health care to everyone in the world,” explains Richard Noble, Engineering Director of Data at Babylon.

Babylon Health’s rapid growth has led it to seek a partner to help it scale.

By partnering with Google Cloud, the company claims that it’s been able to:

• Increase event data ingestion from 1 TB per week to 190 TB daily
• Reduce the wait time for users to access data from six months to a week
• Integrate over 100 data sources – providing access to 80 billion data points
• Save hundreds of hours of work by automatically transcribing 100,000 video consultations in 2021

Babylon Health needs to store and process huge amounts of sensitive data.

“We work with a lot of private patient data and we must ensure that it stays private,” explains Natalie Godec, cloud engineer at Babylon. “At the same time, we must enable our teams to innovate with that data while meeting different national regulatory standards.”

Therefore, Babylon Health required a partner it felt could handle such demands.

“We chose Google Cloud because we knew it could scale with us and support us with our data science and analysis and we could build the tools we needed with it quickly,” added Noble. “It offers the solutions that enable us to focus on our core business, access to health.”

Babylon Health says the move to Google Cloud has enabled it to better analyse its data using AI to unlock new tools and features that help clinicians and users alike. While building a new data model and giving access to users initially took six months, the company says it now takes under a week.

In London, Babylon Health offers its ‘GP at Hand’ service which – in partnership with the NHS – acts as a digital GP practice. Patients can connect to NHS clinicians remotely 24/7 and even be issued prescriptions if required. Where physical examinations are needed, patients will be directed to a suitable venue.

However, GP at Hand has been criticised as “cherry-picking” healthier patients—taking resources away from local GP practices that are often trying to care for sicker, more elderly patients.

Growing pains

While initial problems are to be expected from any relatively new service; poor advice in a healthcare service could result in unnecessary suffering, long-term complications, or even death.

In 2018, Dr David Watkins – a consultant oncologist at Royal Marsden Hospital – reached out to AI News to alert us to Babylon Health’s chatbot giving unsafe advice.

Dr Watkins provided numerous examples of clearly dangerous advice being given by the chatbot:

Babylon Health called Dr Watkins a “troll” who has “targeted members of our staff, partners, clients, regulators and journalists and tweeted defamatory content about us”.

According to Babylon Health, Dr Watkins conducted 2,400 tests of the chatbot in a bid to discredit the service while raising “fewer than 100 test results which he considered concerning”.

Babylon Health claims that in just 20 cases did Dr Watkins find genuine errors while others were “misrepresentations” or “mistakes,” according to Babylon’s own “panel of senior clinicians” who remain unnamed.

Dr Watkins called Babylon’s claims “utterly nonsense” and questions where the startup got its figures from as “there are certainly not 2,400 completed triage assessments”. He estimates conducting between 800 and 900 full triages and that some were repeat tests to see whether Babylon Health had fixed the issues he previously highlighted.

That same year, Babylon Health published a paper claiming that its AI could diagnose common diseases as well as human physicians. The Royal College of General Practitioners, the British Medical Association, Fraser and Wong, and the Royal College of Physicians all issued statements disputing the paper’s claims.

Dr Watkins has acknowledged that Babylon Health’s chatbot has improved and has substantially reduced its error rate. In 2018, when Dr Watkins first reached out to us, he says this rate was “one in one”.

In 2020, Babylon Health claimed in a paper that it can now appropriately triage patients in 85 percent of cases.

Hopefully, the partnership with Google Cloud continues to improve Babylon Health’s abilities to help it achieve its potentially groundbreaking aim to deliver 24/7 access to healthcare wherever a patient is.

(Photo by Hush Naidoo Jade Photography on Unsplash)

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Vertex was announced during this year’s virtual I/O developer conference and somewhat breaks from Google’s tradition of using its keynote to focus more on updates to its mobile and web development solutions. Google announcing the platform during the keynote shows how important the company believes it to be for a wide range of developers.

Google claims that using Vertex enables models to be trained with up to 80 percent fewer lines of code when compared to competing platforms.

Bradley Shimmin, Chief Analyst for AI Platforms, Analytics, and Data Management at Omdia, said:

“Data science practitioners hoping to put AI to work across the enterprise aren’t looking to wrangle tooling. Rather, they want tooling that can tame the ML lifecycle. Unfortunately, that is no small order.

It takes a supportive infrastructure capable of unifying the user experience, plying AI itself as a supportive guide, and putting data at the very heart of the process — all while encouraging the flexible adoption of diverse technologies.”

Vertex brings together Google Cloud’s AI solutions into a single environment where models can go from experimentation all the way to production.

Andrew Moore, VP and GM of Cloud AI and Industry Solutions at Google Cloud, said:

“We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds, and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production.

We are very proud of what we came up with in this platform, as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.”

Vertex provides access to Google’s MLOps toolkit which the company uses internally for workloads involving computer vision, conversation, and language.

Other MLOps features supported by Vertex include Vizier, which increases the rate of experimentation; Feature Store to help practitioners serve, share, and reuse ML features; and Experiments to accelerate the deployment of models into production with faster model selection.

Some high-profile companies were given early access to Vertex. Among them is ModiFace, a part of L’Oréal that focuses on the use of AR and AI to revolutionise the beauty industry.

Jeff Houghton, COO at ModiFace, said:

“We provide an immersive and personalized experience for people to purchase with confidence whether it’s a virtual try-on at web check out, or helping to understand what brand product is right for each individual.

With more and more of our users looking for information at home, on their phone, or at any other touchpoint, Vertex AI allowed us to create technology that is incredibly close to actually trying the product in real life.”

ModiFace uses Vertex to train AI models for all of its new services. For example, the company’s skin diagnostic service is trained on thousands of images from L’Oréal’s Research & Innovation arm and is combined with ModiFace’s AI algorithm to create tailor-made skincare routines.

Another firm that is benefiting from Vertex’s capabilities is Essence, a media agency that is part of London-based global advertising and communications giant WPP.

With Vertex AI, Essence’s developers and data analysts are able to regularly update models to keep pace with the rapidly-changing world of human behaviours and channel content.

Those are just two examples of companies whose operations are already being greatly enhanced through the use of Vertex. Now the floodgates have been opened, we’re sure there’ll be many more stories over the coming years and we can’t wait to hear about them.

You can learn how to get started with Vertex AI here.

(Photo by John Baker on Unsplash)

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The specific GPU added to Google Cloud is the NVIDIA A100 Tensor Core which was announced just last month. NVIDIA says the A100 “has come to the cloud faster than any NVIDIA GPU in history.”

NVIDIA claims the A100 boosts training and inference performance by up to 20x over its predecessors. Large AI models like BERT can be trained in just 37 minutes on a cluster of 1,024 A100s.

For those who enjoy their measurements in teraflops (TFLOPS), the A100 delivers around 19.5 TFLOPS in single-precision performance and 156 TFLOPS for Tensor Float 32 workloads.

Manish Sainani, Director of Product Management at Google Cloud, said:

“Google Cloud customers often look to us to provide the latest hardware and software services to help them drive innovation on AI and scientific computing workloads.

With our new A2 VM family, we are proud to be the first major cloud provider to market NVIDIA A100 GPUs, just as we were with NVIDIA T4 GPUs. We are excited to see what our customers will do with these new capabilities.”

The announcement couldn’t have arrived at a better time – with many looking to harness AI for solutions to the COVID-19 pandemic, in addition to other global challenges such as climate change.

Aside from AI training and inference, other things customers will be able to achieve with the new capabilities include data analytics, scientific computing, genomics, edge video analytics, and 5G services.

The new Ampere-based data center GPUs are now available in Alpha on Google Cloud. Users can access instances of up to 16 A100 GPUs, which provides a total of 640GB of GPU memory and 1.3TB of system memory.

You can register your interest for access here.

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During the Google Cloud Next 18 conference, Google Chief AI Scientist Dr. Fei-Fei Li demonstrated a new AI system called Google Contact Center AI which – much like Duplex – sounded incredibly natural in its responses to human queries.

Google seems to have learned its lesson from its Duplex demonstration and wanted to iterate that it’s not designed to replace human operators.

Instead, the system could be used to replace the current dreaded automated messages you often hear when dialling a call center. “Press 1 for… press 2 for…” just writing it makes me shudder.

Contact Center AI could be able to answer some basic questions about the business to provide answers quickly to callers and reduce the waiting times for those needing a human operator.

When a human operator is necessary, the system could automatically transfer the caller to the correct department.

“Contact Center AI is an example for our passion for bringing AI to every industry all the while elevating the role of human talent,” Li told the audience. “We’re creating technology that’s not just powerful but that’s also trustworthy.”

Call centers are something we’ve all had a frustrating experience with, so it’s a good example of the practical uses of AI when explaining to a wider public that may be sceptical about the technology’s benefits.

Google took the right approach when demonstrating Contact Center AI. The company’s Duplex demo spurred a needed debate about whether an AI should always identify itself as such when interacting with a human.

Several companies, including Google, promise their AIs will do so. Some lawmakers are considering legislation that will make it a legal obligation.

What are your thoughts on Google’s contact center AI? Let us know in the comments.

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