Virgil  Hagenes

Virgil Hagenes

1601733600

Google Announces General Availability Of AI Platform Prediction

Recently, the developers at Google Cloud announced the general availability of the AI Platform Prediction. The platform is based on a Google Kubernetes Engine (GKE) backend and is said to provide an enterprise-ready platform for hosting all the transformative ML models.

Emerging technologies like machine learning and AI have transformed the way most processes and industries work around us. Machine learning has brought various significant features that require predictions, such as identifying objects in images, recommending products, optimising market campaigns and more.

However, building a robust and enterprise-ready machine learning environment can include various issues like it being time-consuming, costly as well as complex. Google’s AI Platform Prediction takes into account all these issues to provide a robust environment for ML-based tasks.

In March this year, the tech giant launched the AI Platform Pipelines in beta version to ensure in delivering an enterprise-ready and a secure execution environment for the machine learning workflows.

According to the developers, this new platform is designed for various functions in machine learning models such as improved reliability, more flexibility via new hardware options such as Compute Engine machine types and NVIDIA accelerators, reduced overhead latency, and improved tail latency.m.

#google ai #google ai platform #google kubernetes #ai

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Google Announces General Availability Of AI Platform Prediction
Virgil  Hagenes

Virgil Hagenes

1601733600

Google Announces General Availability Of AI Platform Prediction

Recently, the developers at Google Cloud announced the general availability of the AI Platform Prediction. The platform is based on a Google Kubernetes Engine (GKE) backend and is said to provide an enterprise-ready platform for hosting all the transformative ML models.

Emerging technologies like machine learning and AI have transformed the way most processes and industries work around us. Machine learning has brought various significant features that require predictions, such as identifying objects in images, recommending products, optimising market campaigns and more.

However, building a robust and enterprise-ready machine learning environment can include various issues like it being time-consuming, costly as well as complex. Google’s AI Platform Prediction takes into account all these issues to provide a robust environment for ML-based tasks.

In March this year, the tech giant launched the AI Platform Pipelines in beta version to ensure in delivering an enterprise-ready and a secure execution environment for the machine learning workflows.

According to the developers, this new platform is designed for various functions in machine learning models such as improved reliability, more flexibility via new hardware options such as Compute Engine machine types and NVIDIA accelerators, reduced overhead latency, and improved tail latency.m.

#google ai #google ai platform #google kubernetes #ai

Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

Archie  Powell

Archie Powell

1625958420

Selecting a Conversational AI Platform

Businesses are quickly acknowledging the importance of Conversational AI (CAI) to increase their customer engagement and revenues. The question is no longer whether to deploy CAI, but rather which platform to use and how to leverage its capabilities.

In this series, Daniel Eriksson, Chief Innovation and Customer Success Officer at Artificial Solutions, gives insight on important aspects of a conversational AI platform that buyers often overlook. For example: what does language support really mean? What is localization? How do different deployment models impact the TCO? And maybe most importantly, how can the CAI platform not only help me during the first development sprints, but across the entire bot lifecycle?

Making Bot Developers More Productive

During the last six months, I’ve had a lot of conversations with companies (clients) and system integrators (partners) who have been building conversational bots. I’ve spoken with conversational bot developers, data linguistics reps, integration engineers, conversational designers, project managers, senior stakeholders, product owners, and many more.

At the same time, I’ve talked to existing, new, prospective, and former clients. These talks included people who had ambitious plans and succeeded and others who have had plans where they have struggled to generate impact.

Four Perspectives to Consider When Selecting your Conversational AI Platform

Select a Tool Your Development Team Can Grow With

See past the buzz-words like “awareness”, “understanding”, and “self-learning”.

Conversational AI is a fascinating space and still holds a lot of potential that is yet to be explored. Yet most companies who have experience of CAI tooling will tell you it’s all about engineering, and actually has a lot of resemblance to regular software or process flow development instead of being something ground-breaking new.

Sure, there are some terminologies both useful and specific for the space, like “intent recognition”, “entities”, and “context”. These words are related to the Natural Language Understanding (NLU) part of a conversational bot.

Find a Balance Between Pure Coding and Drag-and-Drop

Have you ever heard about low-code or no-code? In short, those concepts describe a user interface where a developer can configure or graphically design a process instead of having to write programming code. It is a great way to visualize how a program is executed and can be a quick way to build some things rapidly. Here comes the tricky part — for an effective Conversational AI solution with some ambition, you will still need to code. Your team will need to write code in some scripting language. If not, you will not be able to do the things you expect a bot to do. Do not shy away from this fact, as scripting and coding are super important to make a bot great. So, when you look at a toolset, evaluate it from the standpoint “how will the coding part work?”

Consider Possible Future Limitations

There is a lot of CAI tooling in the market today available to developers. Your job is to make sure that you don’t select tooling that is quick to build only the first MVP but also is useful for every new generation of your bot. When your ambitions grow, and your insights on how you can deliver a better bot user experience start to develop, you might realize that the tool you chose is holding you back.

#ai #artificial intelligence #natural language processing #conversational ai #ai platform #platform

Jeromy  Lowe

Jeromy Lowe

1597776900

Top Google AI, Machine Learning Tools for Everyone

_“We want to use AI to augment the abilities of people, to enable us to accomplish more and to allow us to spend more time on our creative endeavors.” _-- Jeff Dean, Google Senior Fellow

Calling Google just a search giant would be an understatement with how quickly it grew from a mere search engine to a driving force behind innovations in several key IT sectors. Over the past couple of years, Google has planted its roots into almost everything digital, be it consumer electronics such as smartphones, tablets, laptops, its underlying software such as Android and Chrome OS or the smart software backed by Google’s AI.

Google has been actively innovating in the smart software industry. Backed by its expertise in search and analytical data acquired over the years have helped Google create various tools like TensorFlowML KitCloud AI, and many more for enthusiasts and beginners alike who are trying to understand the capabilities of AI.

#ai #automl #data science platforms #datasets #google #google cloud #google colab #machine learning #tensorflow

Otho  Hagenes

Otho Hagenes

1619511840

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution