The cloud-based solution aims to help accelerate the delivery of software in a rapidly-changing world while maintaining a high bar of quality.

Gareth Smith, CTO of Eggplant, said:

“The launch of our cloud platform is a significant milestone in our mission to rid the world of bad software. In our new normal, delivering speed and agility at scale has never been more critical.

Every business can easily tap into Eggplants’ AI-powered automation platform to accelerate the pace of delivery while ensuring a high-quality digital experience.” 

Enterprises have accelerated their shift to the cloud due to the pandemic and resulting increases in things such as home working.

Recent research from Centrify found that 51 percent of businesses which embraced a cloud-first model were able to handle the challenges presented by COVID-19 far more effectively.

Eggplant’s Digital Automation Intelligence (DAI) Platform features:

• Cloud-based end-to-end automation: The scalable fusion engine provides frictionless and efficient continuous and parallel end-to-end testing in the cloud, for any apps and websites, and on any target platforms. 
• Monitoring insights: The addition of advanced user experience (UX) data points and metrics, enables customers to benchmark their applications UX performance. These insights, added to the UX behaviour helps improve SEO. 
• Fully automated self-healing test assets: The use of AI identifies the tests needed and builds and runs them automatically, under full user control. These tests are self-healing, and automatically adapt as the system-under-test evolves.   

The solution helps to support the “citizen developer” movement—using AI to enable no-code/low-code development for people with minimal programming knowledge.

Both cloud and AI ranked highly in a recent study (PDF) by Deloitte of the most relevant technologies “to operate in the new normal”. Cloud and cybersecurity were joint first with 80 percent of respondents, followed by cognitive and AI tools (73%) and the IoT (65%).

Eggplant’s combination of AI and cloud technologies should help businesses to deal with COVID-19’s unique challenges and beyond.

(Photo by CHUTTERSNAP 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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The startup has also developed a cloud-based platform in order to manage some of the challenges that could not be addressed within the car.

According to FiveAI, the latest round of funding will be used to turn these cloud and in-car systems into products. The aim is to later on collaborate with carmakers to develop these platforms into vehicles.

In October, the company announced the launch of commuter research trials for its self-driving car technology along with motor insurance firm Direct Line. As part of the Streetwise Consortium, a 19 kilometre autonomous driving research route that featured human passengers was launched in Croydon and Bromley to test out the technology. Participants were later on asked for insight on their experiences.

FiveAI CEO Stan Boland said at the time: “Shared, self-driving vehicle services promise a better way for people to get around, we’ll be working with forward-thinking partners to make these services a reality in European urban environments. The lessons learned through Streetwise provide an important step towards that goal.”

This is by no means the only funding taking place in this space right now. Waymo announced earlier this month that it had raised $2.25 billion (£1.75bn) in a fundraising round led by Silver Lake, Canada Pension Plan Investment Board and Mubadala Investment Company, while autonomous vehicle technology company Pony.ai secured $400m from Toyota, with the company aiming to strengthen its partnership with Toyota in mobility services.

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During a midnight keynote, Amazon unveiled Transcribe Medical, SageMaker Operators for Kubernetes, and DeepComposer.

Transcribe Medical

The first announcement we’ll be talking about is likely to have the biggest impact on people’s lives soonest.

Transcribe Medical is designed to transcribe medical speech for primary care. The feature is aware of medical speech in addition to standard conversational diction.

Amazon says Transcribe Medical can be deployed across “thousands” of healthcare facilities to provide clinicians with secure note-taking abilities.

Transcribe Medical offers an API and can work with most microphone-equipped smart devices. The service is fully managed and sends back a stream of text in real-time.

Furthermore, and most importantly, Transcribe Medical is covered under AWS’ HIPAA eligibility and business associate addendum (BAA). This means that any customer that enters into a BAA with AWS can use Transcribe Medical to process and store personal health information legally.

SoundLines and Amgen are two partners which Amazon says are already using Transcribe Medical.

Vadim Khazan, president of technology at SoundLines, said in a statement:

“For the 3,500 health care partners relying on our care team optimisation strategies for the past 15 years, we’ve significantly decreased the time and effort required to get to insightful data.”

SageMaker Operators for Kubernetes

The next announcement is Amazon SageMaker Operators for Kubernetes.

Amazon’s SageMaker is a machine learning development platform and this new feature lets data scientists using Kubernetes train, tune, and deploy AI models.

SageMaker Operators can be installed on Kubernetes clusters and jobs can be created using Amazon’s machine learning platform through the Kubernetes API and command line tools.

In a blog post, AWS deep learning senior product manager Aditya Bindal wrote:

“Customers are now spared all the heavy lifting of integrating their Amazon SageMaker and Kubernetes workflows. Starting today, customers using Kubernetes can make a simple call to Amazon SageMaker, a modular and fully-managed service that makes it easier to build, train, and deploy machine learning (ML) models at scale.”

Amazon says that compute resources are pre-configured and optimised, only provisioned when requested, scaled as needed, and shut down automatically when jobs complete.

SageMaker Operators for Kubernetes is generally available in AWS server regions including US East (Ohio), US East (N. Virginia), US West (Oregon), and EU (Ireland).

DeepComposer

Finally, we have DeepComposer. This one is a bit more fun for those who enjoy playing with hardware toys.

Amazon calls DeepComposer the “world’s first” machine learning-enabled musical keyboard. The keyboard features 32-keys and two octaves, and is designed for developers to experiment with pretrained or custom AI models.

In a blog post, AWS AI and machine learning evangelist Julien Simon explains how DeepComposer taps a Generative Adversarial Network (GAN) to fill in gaps in songs.

After recording a short tune, a model for the composer’s favourite genre is selected in addition to setting the model’s parameters. Hyperparameters are then set along with a validation sample.

Once this process is complete, DeepComposer then generates a composition which can be played in the AWS console or even shared to SoundCloud (then it’s really just a waiting game for a call from Jay-Z).

Developers itching to get started with DeepComposer can apply for a physical keyboard for when they become available, or get started now with a virtual keyboard in the AWS console.

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Cloud AI Platform is Google’s machine learning platform-as-a-service (ML PaaS) designed for AI developers, engineers, and data scientists. The platform is end-to-end and supports the full development cycle from preparing data, to training, all the way to building and deploying machine learning models.

Among the most noteworthy additions to the platform is support for Nvidia GPUs. As Google explains, “ML models are so complex that they only run with acceptable latency on machines with many CPUs, or with accelerators like NVIDIA GPUs. This is especially true of models processing unstructured data like images, video, or text.”

Previously, Cloud AI Platform only supported one vCPU and 2GB of RAM. You can now add GPUs, like the inference-optimised, low latency NVIDIA T4, for AI Platform Prediction. The basic tier adds support for up to four vCPUs.

AI Platform Prediction is being used by Conservation International, a Washington.-based organisation with the mission “to responsibly and sustainably care for nature, our global biodiversity, for the wellbeing of humanity,” for a collaborative project called Wildlife Insights.

“Wildlife Insights will turn millions of wildlife images into critical data points that help us better understand, protect and save wildlife populations around the world,” explains Eric H. Fegraus, Senior Director, Conservation Technology.

“Google Cloud’s AI Platform helps us reliably serve machine learning models and easily integrate their predictions with our application. Fast predictions, in a responsive and scalable GPU hardware environment, are critical for our user experience.”

Support for running custom containers in which to train models has also become generally available. Users can supply their own Docker images with an ML framework preinstalled to run on AI Platform. Developers can test container images locally before they’re deployed to the cloud.

Customers aiming to use the platform for inference – hosting a trained model that responds with predictions – can now do so. Machine learning models can be hosted using the Google Cloud AI Platform and AI Platform Prediction can be used to infer target values for obtaining new data.

Oh, and AI Platform Prediction is now built on Kubernetes which enabled Google to “build a reliable and fast serving system with all the flexibility that machine learning demands.”

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If you’ve spent your life binging on sci-fi flicks and shows, today’s AI is far from the likes of HAL and Jarvis. For those kinds of experiences, AGI is needed.

Sam Altman, CEO of OpenAI, said:

“The creation of AGI will be the most important technological development in human history, with the potential to shape the trajectory of humanity. Our mission is to ensure that AGI technology benefits all of humanity, and we’re working with Microsoft to build the supercomputing foundation on which we’ll build AGI.

We believe it’s crucial that AGI is deployed safely and securely and that its economic benefits are widely distributed. We are excited about how deeply Microsoft shares this vision.”

Whereas AI is essentially pre-programmed to perform set tasks, the promise of AGI is to act more like a human brain. Indeed, this is where the excitement of assistants like the aforementioned turn into concerns of a Skynet-like AI as portrayed in Terminator.

Elon Musk founded OpenAI in part due to his concerns that AGI could pose an existential risk if developed carelessly. OpenAI most recently made headlines for developing essentially a fake news generator that, for obvious reasons, it deemed too dangerous to release.

Musk departed OpenAI in February last year over disagreement’s with the company’s development, but the San Francisco-based firm continues to pursue AGI.

Microsoft is clearly on-board with OpenAI’s mission. The $1 billion investment ensures Microsoft becomes OpenAI’s exclusive provider of cloud computing services to OpenAI, and the pair will build new Azure AI supercomputing technologies.

Satya Nadella, CEO of Microsoft, commented:

“AI is one of the most transformative technologies of our time and has the potential to help solve many of our world’s most pressing challenges.

By bringing together OpenAI’s breakthrough technology with new Azure AI supercomputing technologies, our ambition is to democratise AI — while always keeping AI safety front and centre — so everyone can benefit.”

Both companies promise developments will adhere to their ‘shared principles on ethics and trust,’ and will ‘create the foundation for advancements in AI to be implemented in a safe, secure, and trustworthy way’.

The goal of Microsoft and OpenAI is a lofty one that may not even be achieved in many of our lifetimes. In a recent survey of some leading experts in the field, the average estimate was there is a 50 percent chance AGI will be developed by 2099.

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These and many other insights are from an excellent presentation recently given by Kai Gramke, Managing Director of EconSight titled Artificial Intelligence As A Key Technology and Driver of Technological Progress. EconSight clients include the Swiss Federal Council, German Federal Chancellery, leading European think tanks, research institutes and half of the German DAX-30 companies.  The presentation and information shared in this post were generated using the PatentSight analytics platform. PatentSight is a LexisNexis company and you can learn more about them here.  The following are the key takeaways from Kai’s recent research and presentation using PatentSight:

EconSight finds that Microsoft leads the AI patent race going into 2019 with 697 world class patents that the firm classifies as having a significant competitive impact as of November 2018

Out of the top 30 companies and research institutions as defined by EconSight in their recent analysis, Microsoft has created 20% of all patents in the global group of patent-producing companies and institutions. The following graphic provides a comparison of the top 3o in the group. Please click on the graphic to expand it for easier reading.

Machine learning dominates the AI patent landscape today, leading all categories of AI patents including deep learning and neural networks

Machine learning is based on the foundational concepts of Bayesian analysis, data mining, and predictive analytics. Machine learning algorithms and the applications they rely on are designed to find patterns in large-scale data sets, while also being able to solve complex, constraint-based problems by learning from the data.  Enterprise software companies including Microsoft, SAP, and others are actively developing AI technologies that integrate into their existing platforms, streamlining adoption across their many customers. Please click on the graphic to expand for easier reading.

There have been 225,833 AI-based patents filed globally since 2000, with 30.7% being industry specific (Industry 4.0 on the graphic below) followed by health-related patents (28.1%) 

13.8% of all AI-based patents are for digital security and 11.9% for energy. It’s interesting to note that the fastest growing patents between 2000 and 2018 are for applying AI to marketing (22% CAGR) and AI-based digital security (18.8% CAGR). Please click on the graphic to expand for easier reading.

/">AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, , & .

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The Box Skills framework was first announced in 2017 and was developing an additional layer called ‘Box Skills Kit’ since inception. The latter is a toolkit that allows companies to develop their own bespoke versions of these integrations. The toolkit has attracted development from the likes of IBM, Microsoft, Google, Deloitte, and AIM Consulting.

Chief product officer at Box, Jeetu Patel, said: “Artificial intelligence has the potential to unlock incredible insights, and we are building the world’s best framework, in Box Skills, for bringing that intelligence to enterprise content.”

The Skills Kit has already been used by spirits firm Remy Cointreau. This work involved taking the basic Box Skill for automatically matching metadata to uploaded images, and modifying it so that it would identify specific company products in images. This is how the uploaded images are sorted into specific folders without the need for human interaction or verification.

Box also revealed that its Box Skills platform, which earlier only offered pre-built AI integrations, can now host custom AI models built by third-party AI firms. This means that if an organisation prefers a specific machine learning model built by IBM Watson Studio, Google Cloud AutoML, Microsoft Azure Custom Vision, or AWS SageMaker, can now be integrated into the Box platform to utilise the stored data.

The company also announced updates to its core automation services, which now enables customers to build their own scripts for repetitive workloads. For instance, a marketing team could automate the creation of a template at the beginning of every month and notify specific users to begin collaborating on a new pitch.

Box’s solution appears to be aimed towards smaller work groups that have predictable repetitive tasks in between periods of ad hoc collaboration. It’s less suited for more complicated tasks or those which are unpredictable.

The dashboard for creating these pre-scripted events is very simple, as every automation is based on the premise of ‘if this, then that’. This means that automated processes can be designed quickly by using the drop-down menus.

Box Skills supports over 20 different types of input and output, and includes options for targeting metadata, specific files, or entire folders.

Are you looking forward to the release of Box Skills?

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Trudel added, “We’re starting to see AI having a significant impact on cloud computing. If you extrapolate what the analysts are saying, there’s faster growth in AI, with 10% of cloud revenue expected to come from AI by 2025.”

Trudel noted that although major cloud suppliers are already offering AI capabilities, cloud-based AI services market is still at a nascent stage. He said AWS’s approach to AI is interesting because it uses AI to help organisations easily determine the best open-source machine learning frameworks, such as TensorFlow or MXNet to use for crunching different types of data. AWS also offers Common Crawl, a publicly available dataset that contains petabytes of data collected over years of web crawling, Trudel said.

Trudel noted that Google has been doing a lot of work in AI, whether it is “helping developers choose the best algorithm for AI projects, using its Deepmind technology to build AI services or open-sourcing TensorFlow and Android to gain mindshare in AI developments”. Additionally, Google is also using AI to improve the energy efficiency of its datacentres. “Now, national grid providers are also looking at leveraging the same [AI] models to drive efficiency in their networks,” he said.

Trudel said that major cloud suppliers are working towards delivering general AI services. He is also expecting companies such as Apple, Baidu, Alibaba, Intel, Tencent and Facebook to join the fray with their own AI services.

Do you agree with Trudel’s comments?

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