Finally, Tech Giants are turning down Unethical AI Projects

Tech giants

The pros of artificial intelligence technology have always been followed up with the cons of leveraging these technologies. With the launch of every new system that requires users to share personal and biometric data, a school of thought has emerged voicing the ethical and privacy concerns of using the systems. A recent investigative report by Reuters sheds light on how the three US tech giants — Google, IBM and Microsoft — have been resisting and turning down projects on account of ethics concerns. 

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Easing Machine Learning for developers, Google’s Vertex AI managed ML Platform now available

Vertex AI

Unveiled at the company’s Google I/O virtual conference on May 18 (Tuesday), Vertex AI uses almost 80 percent fewer lines of code to train a model compared to competing platforms, according to Google. The platform is also built to serve data scientists and ML engineers so they can use it to implement and manage machine learning operations (MLOps) for their projects.

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Best MLOps Platforms to Manage Machine Learning Lifecycle

MLOps Platforms

The machine learning lifecycle is the process of developing machine learning projects in an efficient manner. Building and training a model is a difficult, long process, but it’s just one step of your whole task. There’s a long process behind the machine learning lifecycle: collecting data, preparing data, analyzing, training, and testing the model. 

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Google Cloud and Siemens boosting AI processes for Manufacturing in new collaboration

Robotic assembly

Developing and integrating new AI innovations to improve industrial manufacturing processes and productivity on factory floors is the aim of a new partnership between Google Cloud and Siemens AG. The companies will combine Google Cloud’s data cloud and artificial intelligence and machine learning capabilities with Siemens’ broad range of industrial automation products to build scalable and targeted AI services that can help manufacturers around the world improve their processes.

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Google makes business process tool AppSheet Automation generally available

Google appsheet automation

Last year, Google launched AppSheet Automation, an “intent-driven” experience in Google Cloud powered by AI that enabled enterprises to connect to a number of data sources to model automated business processes. After several months in early access, Google today announced that AppSheet Automation is generally available with new capabilities, including document processing, a monitoring app, and expanded eventing support.

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Using Google’s Speech-to-Text API with Python

In this post, I will share the steps to implement the Google Speech-to-Text API using python and tips I learned in the process. There are 3 methods of transcription with Google’s API shown below. For this post, I will be focusing on the asynchronous recognition type using the REST API. Requests are made to the API and a response is returned in the form of a JSON.

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Google partners with Automation Anywhere to develop RPA products

Google RPA

Google today announced a strategic, multi-year collaboration with robotic process automation (RPA) startup Automation Anywhere to accelerate the adoption of RPA with enterprises “on a global scale.” The partnership will make the Automation Anywhere platform available on Google Cloud and see the two companies mutually develop AI- and RPA-powered solutions, closely aligning go-to-market teams.

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Google launches suite of AI-powered solutions for retailers

Fashion and AI

Google today announced the launch of Product Discovery Solutions for Retail, a suite of services deigned to enhance retailers’ ecommerce capabilities and help them deliver personalized customer experiences. Product Discovery Solutions for Retail brings together AI algorithms and a search service, Cloud Search for Retail, that leverages Google Search technology to power retailers’ product-finding tools.

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Google launches AI Platform Prediction in general availability

Google today launched AI Platform Prediction in general availability, a service that lets developers prep, build, run, and share machine learning models in the cloud. It’s based on a Google Kubernetes Engine backend and features an architecture designed for high reliability, flexibility, and low overhead latency.

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