Episode 1:  Is AI coming for your job?

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IBM is offering an AI development studio with access to IBM-curated and trained foundation models and open-source models, access to a data store so users can gather and cleanse training and tuning data, and a toolkit for governance of AI aimed at making AI more easy to adapt and scale.

The IBM watsonx platform is made up of three product sets: IBM watsonx.ai, IBM watsonx.data, and IBM watsonx.governance. All of these product sets are geared at addressing the need for businesses to build their own AI models, or fine-tune available models, on their own data and deploy them at scale.

“With the development of foundation models, AI for business is more powerful than ever,” said Arvind Krishna, chairman and CEO of IBM. “Foundation models make deploying AI significantly more scalable, affordable, and efficient. We built IBM watsonx for the needs of enterprises, so that clients can be more than just users, they can become AI advantaged. With IBM watsonx, clients can quickly train and deploy custom AI capabilities across their entire business, all while retaining full control of their data.”

IBM watsonx.ai is expected to be available in July 2023 and provides users with a next-generation enterprise studio that allows AI builders to train, test, tune, and deploy traditional machine learning as well as newer generative AI capabilities through an open user interface.

Next, IBM watsonx.data, also expected in July 2023, is a fit-for-purpose data store built on open lakehouse that is intended for governed data and AI workloads, supported by querying, governance, and open data formats to access and share data.

Lastly, IBM watsonx.governance can be expected later this year, and it is an AI governance toolkit that is geared at enabling trusted AI workflows. It operationalizes governance to help reduce the risk, time, and cost that comes with manual processes, and offers capabilities to protect user privacy, detect model bias and drift, and help organizations meet ethics standards. 

IBM also plans to integrate watsonx.ai foundation models into all its major software products going forward, including:

• Watson Code Assistant: A solution that accesses generative AI to allow for the generation of code with an English language command.
• AIOps Insights: AIOps features with foundation models for code and NLP to offer heightened visibility into performance across IT environments.
• Watson Assistant and Watson Orchestrate: IBM’s digital labor products which are expected to be combined with an NLP foundation model to help improve employee productivity as well as user experiences 
• Environmental Intelligence Suite: IBM EIS Builder Edition is intended to allow organizations to create solutions to address and mitigate environmental risks based on their own goals.

To learn more, visit the website. 

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Microsoft also announced that it’s moving from text-only search & chat to one that’s more visual with rich image/video answers and new multimodal support coming shortly. Users can get more visual answers including charts and graphs and updated formatting of answers, to help them find information more easily. Image Creator has also been expanded to all languages in Bing.

Microsoft Edge will be redesigned with a sleeker and enhanced UI and is adding the ability to incorporate visual search in chat so that users can upload images and search the web for related content.

Chat history allows users to pick up where they left off and return to previous chats in Bing chat with chat history. Chats can then be moved to Edge Sidebar so that they can be kept on hand while browsing. 

Microsoft stated that it will soon add export and share functionalities into chat for times when people want to easily share conversations with others on social media.

“The new AI-powered Bing has already helped people more easily find or create what they are looking for, making chat a great tool for both understanding and taking action. The integration of Image Creator saves you time by completing the task of creating the image you need right within chat,”  Yusuf Mehdi, corporate vice president and consumer chief marketing officer wrote in a blog post that contains additional details on the new features. 

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“GitLab’s vision for generative AI is grounded in privacy, security, and transparency. Our partnership with Google Cloud enables GitLab to offer private and secure AI-powered features, while maintaining customer data in our cloud infrastructure,” said David DeSanto, chief product officer at GitLab. “This allows us to harness the power of Google Cloud, while continuing GitLab’s privacy-first approach to customer data. We look forward to our continued collaboration to deliver enterprise-grade AI-assisted functionalities to joint customers.” 

One of the new features is called “Explain this Vulnerability,” which provides natural language descriptions of vulnerabilities and also offers recommendations on what to do about them.

GitLab also has some existing AI features in its platform, such as Code Suggestions and Suggested Reviewers. There are also developer productivity features like Explain this Code, Summarize Issue Comments, and Summarize Merge Request Changes. 

The hope that GitLab has with this partnership is to improve “DevSecOps workflow efficiency by 10x.” According to a recent survey by the company, 62% of developers used AI to check code in 2023, which was up from just 51% in 2022. They also found that 36% use AI for code review. 

“Organizations today are required to deliver software faster than ever before to remain competitive while requiring a stronger security posture in order to maintain customer, investor, and stakeholder trust,” said June Yang, VP of Cloud AI and Industry Solutions at Google Cloud. “Together with GitLab, we’ll be able to deliver generative AI functionality that empowers our joint customers to increase delivery velocity without sacrificing security.”

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This tool is designed to replace legacy testing grids and add AI-powered self healing capabilities that work towards augmenting open-source test frameworks. These capabilities heal broken tests intelligently as they run, which reduces flakiness and execution time. 

According to the company, this release locates and solves issues continuously, so the user can be sure that tests are running smoothly and reliably. 

It is also intended to be scalable and affordable so that testing teams can test as much or as little as necessary, without the need to worry about bottlenecks or performance issues.

“Applitools’ self-healing Execution Cloud is a game-changing solution that will revolutionize the way companies of any size test their software at scale,” said Adam Carmi, co-founder and CTO of Applitools. “Teams that use open-source test frameworks can now execute their tests in the cloud at scale and take advantage of the latest and greatest AI capabilities that ensure their tests are automatically healed, saving valuable time and effort.”

Applitools Execution Cloud is also compatible with popular open-source frameworks such as Selenium and WebdriverIO. It can also run existing tests without the need for code changes.

To sign up, visit the website. 

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Now, Microsoft is making it easier for companies to do so with the release of Semantic Kernel’s Copilot Chat sample app. Developers can use the new tool to integrate large language models (LLMs) into their applications. 

RELATED CONTENT: SD Times Open-Source Project of the Week: Semantic Kernel

The sample app enables multiple conversation topics, speech recognition, file uploads, persistent memory storage, and downloadable bots that can be shared with others. 

According to Microsoft, some ideas for apps that can be created are a customer service tool, personalized recommendation system, HR assistant, educational tool, or e-commerce assistant. 

Benefits include improved user experience because information is more easily obtained, improved efficiency by removing the need for human intervention, improved accessibility by providing personalized assistance to users based on their needs, and the ability to easily scale to meet demand. 

“By providing personalized assistance and natural language processing, your own chatbot can improve the user experience for customers, students, and employees alike. Users can get the information they need quickly and easily, without having to navigate complex websites or wait for assistance from a customer service representative,” Shannon Monroe, principal program manager at Microsoft, wrote in a blog post. 

Like the Semantic Kernel that it is built upon, Copilot Chat is open-source and can be accessed through GitHub.  

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The accelerator will provide a 10-week program for 8-12 startups and will offer them access to Google Cloud resources, mentorship, technical expertise, and networking opportunities. 

Startup founders and leaders can also benefit from deep dives and workshops on product design, customer acquisition, and leadership development. 

“Thanks to truly amazing mentorship and direct access to Googlers, we have been able to reach new levels of specialized knowledge and deployment capability in our GCP architecture and artificial intelligence projects. From a technical perspective to a business growth standpoint, this is simply invaluable,” said Francois Gand, founder, and CEO at NURO. “What we have built in three months with Google will be a part of our upcoming next-gen product line in both Healthcare and Non-Healthcare settings. We deeply thank all Googlers for their exceptional participation in our journey.”

Applications are now being accepted until May 30, and the Accelerator will kick off this July. Google has already partnered with digital-native companies such as cart.com to democratize e-commerce and with startups like kimo.ai which leverages tools to transform traditional approaches to online learning. 

“Around the world, the cloud is helping businesses and governments accelerate their digital transformations, scale their operations, and innovate in new areas. At Google Cloud, we’re helping businesses solve some of their toughest challenges,” Google stated in a blog post. 

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With Corbett, teams gain access to intelligence features that allow for the leveraging of AI to deliver improved software. Digital.ai now integrates and centralizes data from more sources from development through to production, so users can avoid ruined reports and analytics and use AI to predict possible outcomes based on past results.

This release brings new persona-based dashboards that can be used to analyze data for every stage of application development and delivery while also showing performance indicators for team and princess efficiencies as well as risk reduction. 

The company stated that this expands on the existing predictive intelligence capabilities that apply machine learning to help teams assess alternatives, manage tradeoffs, and make choices in a more predictive way.

“The release of Corbett underscores our commitment to provide an open DevOps platform built for the enterprise and to deliver targeted solutions that support the complexity and scale of the world’s largest organizations,” said Derek Holt, CEO of Digital.ai. “With the Corbett release we take another big step forward as we deliver all new versions of our market-leading offerings and dramatically enhance the role of data, ML and AI within software development and delivery. As the pace of AI-based innovation continues to accelerate we are excited to harness the power of AI to unlock value not just for individuals, but for teams and organizations.”

Among the key enhancements included in Corbett is an improved Jailbreak Bypass Detection so that users can automatically detect the newest bypasses. This works to frustrate attackers and prevent jailbreaks.

Furthermore, teams get Release Manager and Platform Engineering Intelligence which works to cut back on risk by analyzing release dependencies and success trends to improve upon the identification of why releases might fail.

Lastly, Corbett offers the ability to integrate security checks into release pipelines for better governance. This lets organizations automatically check whether or not a list of protections has been applied to a mobile app by utilizing a new integration with OPA.

To learn more, read the blog post. 

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The software includes all the code, examples, and documentation businesses need to add safety to AI apps that generate text. NVIDIA said it’s releasing the project since many industries are adopting large language models (LLMs), the powerful engines behind these AI apps. 

Users can set up three kinds of boundaries with NeMo Guardrails: topical, safety, and security. 

With topical guardrails, apps can be prevented from going into unwanted areas by implementing topical guardrails. An instance of this is preventing customer service assistants from responding to inquiries regarding the weather.

Safety guardrails ensure apps respond with accurate, appropriate information. They can filter out unwanted language and enforce that references are made only to credible sources.

Security guardrails restrict apps to make connections only to external third-party applications known to be safe.

The tool is compatible with the tools that enterprise app developers commonly use. For example, it is capable of functioning on LangChain, an open-source toolkit that developers are readily embracing to incorporate third-party applications with LLMs. Furthermore, NeMo Guardrails is designed to be versatile enough to function with a wide range of LLM-enabled applications, including Zapier.

The project is being incorporated into the NVIDIA NeMo framework that already has open-source code on GitHub.

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These features have been launched in over 20 different programming languages, including C++, Go, Java, Javascript, Python, and Typescript. Users can also export Python code to Google Colab without needing to copy and paste. 

Bard can also now help write functions for Google Sheets and help explain code snippets for the customer. This update also enables Bard to help with the debugging of code, even code that Bard wrote itself. 

According to Google, if the user receives an error message, they can inform Bard that “this code didn’t work, please fix it,” and the tool will help debug.

In some instances, the tool can also help improve code by making it quicker or more efficient. Users can make this happen by responding to Bard’s initial output with “could you please make that code faster?” or “find error handling causes you might have missed.”

The company warned, however, that Bard is still an early experiment, and may provide inaccurate, misleading, or false information. There is still a risk that Bard may produce working code that does not provide the expected output or code that is not optimal or complete, so it is important for users to always double check the code themselves.

For more information, read the blog. 

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