“For most software developers, every day starts with where the last one left off. Trawling through the backlog of issues on GitHub you didn’t deal with the day before, you’re triaging which ones you can fix quickly, which will take more time, and which ones you really don’t know what to do with yet. You might have 30 issues in your backlog and know you only have time to tackle 10,” IBM wrote in a blog post. This new family of agents aims to alleviate this burden and shorten the time developers are spending on these tasks. 

One of the agents is a localization agent that can find the file and line of code that is causing an error. According to IBM, the process of finding the correct line of code related to a bug report can be a time-consuming process for developers, and now they’ll be able to tag the bug report they’re working on in GitHub with “ibm-swe-agent-1.0” and the agent will work to find the code. 

Once found, the agent suggests a fix that the developer could implement. At that point the developer could either fix the issue themselves or enlist the help of other SWE agents for further assistants. 

Other agents in the SWE family include one that edits lines of code based on developer requests and one that can be used to develop and execute tests. All of the SWE agents can be invoked directly from within GitHub.

According to IBM’s early testing, these agents can localize and fix problems in less than five minutes and have a 23.7% success rate on SWE-bench tests, a benchmark that tests an AI system’s ability to solve GitHub issues. 

IBM explained that it set out to create SWE agents as an alternative to other competitors who use large frontier models, which tend to cost more. “Our goal was to build IBM SWE-Agent for enterprises who want a cost efficient SWE agent to run wherever their code resides — even behind your firewall — while still being performant,” said Ruchir Puri, chief scientist at IBM Research.

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“Reflecting our focus on the balance between powerful and practical, the new IBM Granite 3.0 models deliver state-of-the-art performance relative to model size while maximizing safety, speed and cost-efficiency for enterprise use cases,” IBM wrote in a blog post.

The Granite 3.0 family includes general purpose models, more guardrail and safety focused ones, and mixture-of-experts models. 

The main model in this family is Granite 3.0 8B Instruct, an instruction-tuned, dense decoder-only model that offers strong performance in RAG, classification, summarization, entity extraction, and tool use. It matches open models of similar sizes on academic benchmarks and exceeds them for enterprise tasks and safety, according to IBM.

“Trained using a novel two-phase method on over 12 trillion tokens of carefully vetted data across 12 different natural languages and 116 different programming languages, the developer-friendly Granite 3.0 8B Instruct is a workhorse enterprise model intended to serve as a primary building block for sophisticated workflows and tool-based use cases,” IBM wrote.

This release also includes new Granite Guardian models that safeguard against social bias, hate, toxicity, profanity, violence, and jailbreaking, as well as perform RAG-specific checks like groundedness, context relevant, and answer relevance.  

There are also a number of other models in the Granite 3.0 family, including: 

• Granite-3.0-8B-Base, Granite-3.0-2B-Instruct and Granite-3.0-2B-Base, which are general purpose LLMs
• Granite-3.0-3B-A800M-Instruct and Granite-3.0-1B-A400M-Instruct, which are Mixture of Experts models that minimize latency and cost
• Granite-3.0-8B-Instruct-Accelerator, which are speculative decoders that offer better speed and efficiency

All of the models are available under the Apache 2.0 license on Hugging Face, and Granite 3.0 8B and 2B and Granite Guardian 3.0 8B and 2B are available for commercial use on watsonx. 

The company also revealed that by the end of 2024, it plans to expand all model context windows to 128K tokens, further improve multilingual support, and introduce multimodal image-in, text-out capabilities. 

And in addition to releasing these new Granite models, the company also revealed the upcoming availability of the newest version of the watsonx Code Assistant, as well as plans to release new tools for developers building, customizing, and deploying AI through watsonx.ai.

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“The official publication of these algorithms marks a crucial milestone to advancing the protection of the world’s encrypted data from cyberattacks that could be attempted through the unique power of quantum computers, which are rapidly progressing to cryptographic relevancy. This is the point at which quantum computers will harness enough computational power to break the encryption standards underlying most of the world’s data and infrastructure today,” IBM, who developed two of these new standards, wrote in a statement. 

RELATED: What NIST’s newly approved post-quantum algorithms mean for the future of cryptography

According to NIST, experts predict that we may reach that point of cryptographic relevancy within the next decade. This set of three new algorithms use different math problems that even a quantum computer would have trouble solving, NIST explained.

These new algorithms are part of NIST’s post-quantum cryptography (PQC) program, and today’s news comes eight years after NIST first announced a call for proposals asking for standards and strategies for securing information in a quantum world. 

“Quantum computing technology could become a force for solving many of society’s most intractable problems, and the new standards represent NIST’s commitment to ensuring it will not simultaneously disrupt our security,” said Laurie E. Locascio Under Secretary of Commerce for Standards and Technology and NIST Director. “These finalized standards are the capstone of NIST’s efforts to safeguard our confidential electronic information.”

NIST is also still evaluating two other sets of algorithms, and plans to select one or two of them by the end of the year. The first set contains algorithms with a different type of math problems than the selected one, and the second set contains algorithms that are designed for digital signatures.

In addition, NIST expects to announce 15 algorithms that it received during a second call for proposals in 2022. 

However, even though NIST is still approving additional algorithms, it says that they consider them as backups and recommends technologists not postpone using the three that have already been announced. 

“We need to be prepared in case of an attack that defeats the algorithms in these three standards, and we will continue working on backup plans to keep our data safe,” said Dustin Moody, a NIST mathematician who heads the PQC program. “But for most applications, these new standards are the main event.” 

Tom Patterson, emerging technology security lead at Accenture, believes that this announcement “marks a pivotal moment in our cybersecurity landscape. As quantum computers emerge, they present a significant risk to our current encryption methods. Organizations must assess their quantum risk, discover vulnerable encryption within their systems, and develop a resilient cryptographic architecture now.”

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One of the points CEO Arvind Krishna touched on in his opening keynote is the desire to invest in and contribute to the open-source AI community. 

“We firmly believe in bringing open innovation to AI. We want to use the power of open source to do with AI what was successfully done with Linux and OpenShift,” said Krishna. “Open means choice. Open means more eyes on the code, more minds on the problems, and more hands on the solutions. For any technology to gain velocity and become ubiquitous, you’ve got to balance three things: competition, innovation, and safety. Open source is a great way to achieve all three.”

To that end, IBM has open sourced a new set of its Granite AI models, which can be used for tasks like application modernization, code generation, troubleshooting, documentation, and maintaining repositories, among other things. 

The models are now available on Hugging Face and GitHub, ranging from 3B to 34B parameter varieties. 

Additionally, the company, in partnership with Red Hat, launched InstructLab, which is a model alignment technique that enables developers to create models that are specific to their own companies or domains. An enterprise-ready version of InstructLab is available as part of RHEL AI. 

“It can enhance an LLM using far less human-generated information and far fewer computing resources than are typically used to retrain a model. And it makes it possible for upstream contributions to continuously make the model better,” IBM wrote in a blog post. 

The company also shared several upcoming improvements to watsonx. It will be releasing the watsonx Code Assistant for Enterprise Java Applications in October, watsonx Assistant for Z in June, and will expand watsonx Code Assistant for Z Service that same month to offer code explanation capabilities.

IBM will also add an AI assistant builder in watsonx Orchestrate, which will provide a low-code interface for creating new AI assistants. “The AI assistant builder enables you to extend your investments in automation and scale adoption through a highly conversational interface that is infused with AI that you can trust,” IBM wrote in a blog post. 

Next month, the company will also release IBM Concert, which uses generative AI to provide insights from a company’s applications, cloud infrastructure, source repositories, and CI/CD pipelines. It will help customers identify, predict, and respond to issues before they happen, according to IBM. 

“Powered by AI from watsonx, Concert can generate analyses, visualizations and recommendations that you can quickly turn into action. By helping you discover gaps, prioritize insights and instrument changes, Concert helps to reduce complexity and streamline operations so you can make your business more resilient, more innovative, and more cost-effective,” IBM wrote in a blog post. 

And finally, IBM announced it is continuing to improve collaborations with a number of companies to expand model choice. These include AWS, Adobe, Meta, Microsoft, Mistral, Palo Alto Networks, Salesforce, SAP, and SDAIA. 

“Our ecosystem of partners, large and small, are helping clients adopt and scale tailored AI across their businesses,” IBM said. 

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IBM says that the goal with this acquisition is to create “a comprehensive end-to-end hybrid cloud platform.”

HashiCorp’s portfolio includes a number of popular tools, including Terraform for infrastructure as code provisioning, Vault for secrets management, Consul for service-based networking, and more.

According to a statement from HashiCorp, it will continue to operate under the HashiCorp name as a division within IBM Software. Armon Dadgar, co-founder and CTO of HashiCorp, said that by joining IBM, it will be able to offer its products to a much wider audience. 

“While we are more than a decade into HashiCorp, we believe we are still in the early stages of cloud adoption. With IBM, we have the opportunity to help more customers get there faster, to accelerate our product innovation, and to continue to grow our practitioner community,” Dadgar wrote. 

Dave McJannet, CEO of HashiCorp, added: “IBM’s leadership in hybrid cloud along with its rich history of innovation, make it the ideal home for HashiCorp as we enter the next phase of our growth journey. I’m proud of the work we’ve done as a standalone company, I am excited to be able to help our customers further, and I look forward to the future of HashiCorp as part of IBM.”

Kris Beevers, co-founder and CEO of NetBox Labs, believes that with this acquisition IBM is trying to consolidate ownership of two of the most popular open source IT automation tools: Red Hat Ansible (acquired in 2019) and now HashiCorp Terraform. 

“In network management and automation specifically, Ansible and Terraform dominate the ecosystem and are widely deployed by practitioners,” said Beevers. “This move will make IBM an open source IT automation powerhouse. I expect the consolidation of these tools under the IBM umbrella might result in more whitespace for new open source automation tools over time, but in the near term it will simplify the ecosystem and accelerate vendor and open source integrations with Ansible and Terraform, which will simplify and accelerate enterprise IT and network automation initiatives.”

Though widely used, Terraform hasn’t had a perfect year, after last August when HashiCorp announced that Terraform would switch from the Mozilla Public License 2.0 to the Business Source Licenses for its future releases. In response, the Terraform community created an open fork of Terraform, called OpenTofu.

When the change was first announced, the OpenTofu community wrote the OpenTofu Manifesto, stating ”In our opinion, this change threatens the entire community and ecosystem that’s built up around Terraform over the last 9 years.”

Then earlier this month OpenTofu received a cease and desist from HashiCorp because of copyright claims, and OpenTofu has denied those claims. The cease and desist claimed that OpenTofu copied code that was under the BSL, however OpenTofu denied this, offering the explanation that both HashiCorp and OpenTofu copied the code from the MPL v2.0 version. 

“HashiCorp has made claims of copyright infringement in a cease & desist letter. These claims are completely unsubstantiated. The code in question can be clearly shown to have been copied from older code under the MPL-2.0 license. HashiCorp seems to have copied the same code itself when they implemented their version of this feature,” OpenTofu wrote in a response. 

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By bringing together a diverse set of types of organizations, the AI Alliance hopes to shape AI’s evolution in a way that reflects the ranging needs of society. 

It’s starting off with a number of projects, including developing benchmarks and evaluation standards, responsibly advocating for open foundation models with diverse modalities, enabling a strong AI hardware accelerator ecosystem, supporting global AI skills building, developing educational content and resources, and launching initiatives to support open development of AI in safe ways, including hosting events to explore AI use cases. 

“The AI Alliance is focused on fostering an open community and enabling developers and researchers to accelerate responsible innovation in AI while ensuring scientific rigor, trust, safety, security, diversity and economic competitiveness. By bringing together leading developers, scientists, academic institutions, companies, and other innovators, we will pool resources and knowledge to address safety concerns while providing a platform for sharing and developing solutions that fit the needs of researchers, developers, and adopters around the world,” the AI Alliance wrote in a press release. 

Other starting members include AMD, Anyscale, CERN, Cerebras, Cleveland Clinic, Cornell University, Dartmouth, Dell Technologies, EPFL, ETH, Hugging Face, Imperial College London, Intel, INSAIT, Linux Foundation, MLCommons, MOC Alliance operated by Boston University and Harvard University, NASA, NSF, Oracle, Partnership on AI, Red Hat, Roadzen, ServiceNow, Sony Group, Stability AI, University of California Berkeley, University of Illinois, University of Notre Dame, The University of Tokyo, Yale University and others. 

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This tool aims to address challenges associated with generative AI, which is powered by large language models (LLM) or foundation models. While such AI models offer various business use cases, they also bring risks and complexities, such as the use of unverified training data and the generation of outputs that lack explainability, IBM explained. 

Watsonx.governance is designed to help organizations manage these risks, enhance transparency, and prepare for compliance with future regulations focused on AI.

“Company boards and CEOs are looking to reap the rewards from today’s more powerful AI models, but the risks due to a lack of transparency and inability to govern these models have been holding them back,” said Kareem Yusuf, Ph.D, senior vice president of product management and growth at IBM Software. “Watsonx.governance is a one-stop-shop for businesses that are struggling to deploy and manage both LLM and ML models, giving businesses the tools, they need to automate AI governance processes, monitor their models, and take corrective action, all with increased visibility. Its ability to translate regulations into enforceable policies will only become more essential for enterprises as new AI regulation takes hold worldwide.”

IBM Consulting has broadened its strategic capabilities to assist clients in responsibly scaling AI. This involves automated model governance and organizational governance covering people, processes, and technology. 

IBM consultants possess expertise in establishing AI ethics boards, shaping organizational culture, providing training, managing regulations and risks, and addressing cybersecurity threats, all through human-centric design. Watsonx.governance is part of the IBM watsonx AI and data platform, which includes AI assistants, the watsonx.ai enterprise studio, and the watsonx.data governed data store. Additionally, IBM has introduced intellectual property protection for its watsonx models.

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According to IBM, the approach it is taking with the Enterprise AI Venture Fund is unique because it combines domain expertise from across IBM and a dedicated portfolio development team to invest in AI companies that are making a significant impact in the business world. 

The fund will offer startups the chance to form partnerships with IBM, receive operational guidance, and enhance their product and engineering strategies. IBM, with its established history in enterprise AI, is leveraging its expertise and the latest generative AI innovations to expand its AI partnerships and create value for AI leaders and clients, both new and existing.

“AI is slated to unlock nearly $16 trillion in productivity by 2030. With the launch of the IBM Enterprise AI Venture Fund, we’re opening another channel to harness the enormous potential of the AI revolution into tangible, positive outcomes for IBM and the companies we invest in,” said Rob Thomas, the senior vice president of software and chief commercial officer at IBM. “This fund is yet another way we’re doubling down on our commitment to responsible AI innovation through watsonx and helping organizations put this transformational technology to work.”

IBM stated that it is dedicated to advancing AI for business by investing in AI technology and companies. It recently participated in a significant funding round for Hugging Face, a prominent open-source platform for the machine learning community. IBM has also made substantial contributions to Hugging Face, including the release of the Geospatial Foundation Model developed in collaboration with NASA. This model is the largest of its kind on Hugging Face and represents the first open-source AI foundation model created in partnership with NASA.

IBM has joined the Series A funding of HiddenLayer, a top security provider for AI models and assets. This funding is aimed at expanding HiddenLayer’s talent pool, enhancing its go-to-market strategies, and further developing its acclaimed Machine Learning Security (MLSec) Platform.

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Watsonx Code Assistant for Red Hat Ansible Lightspeed uses generative AI to assist IT teams with things like network configuration or code deployment. Watsonx Code Assistant for Z uses the technology to help with mainframe application modernization and can translate applications from COBOL to Java. 

RELATED CONTENT: IBM releases watsonx Code Assistant for Red Hat Ansible Lightspeed

Watsonx Code Assistant for Z can help with application discovery, business services refactoring, and code transformation. It plans to add automated validation testing to the tool in the future. 

IBM explained that the goal here isn’t to completely turn all the COBOL applications into Java applications, but rather enable a world where the two are used optimally together. 

“Simply put, each language brings their own unique strengths and clients should have full control over which language they want to use for their business needs. Independent of client choice, IBM supports interoperability and optimization for IBM Z capabilities, and you also can leverage your existing application investments,” Kyle Charlet, IBM Fellow and CTO of Z Software at IBM, wrote in a post.  

The company is exploring how to expand the tool to additional programming languages in the future. 

Watsonx Code Assistant is based on the Granite foundation model, which is IBM’s own model. IBM is in the process of working on figuring out new opportunities to train the model to incorporate domain-specific knowledge so that it can expand the generative AI capabilities to more areas, such as code generation and code explanation. 

“With this launch, watsonx Code Assistant joins watsonx Orchestrate and watsonx Assistant in IBM’s growing line of watsonx assistants that provide enterprises with tangible ways to implement generative AI,” said Kareem Yusuf, Ph.D, senior vice president of product management and growth at IBM Software. “Watsonx Code Assistant puts AI-assisted code development and application modernization tools directly into the hands of developers – in a naturally integrated way that is designed to be non-disruptive – to help address skills gaps and increase productivity.”

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These models come in various sizes to cater to different business needs and are based on a decoder-only architecture. They can be used to scale AI in various ways, such as generating customized responses from enterprise knowledge bases, summarizing lengthy content like contracts or call transcripts, and extracting insights and classifying data, including customer sentiment.

In addition to its own models, IBM provides flexibility to use third-party models like Meta’s Llama 2-chat 70 billion parameter model and models from the Hugging Face community.

“When it comes to today’s AI innovation boom, the businesses that are positioned for success are the ones outfitted with AI technologies that demonstrate success at scale and have built-in guardrails and practices that enable their responsible use,” said Dinesh Nirmal, senior vice president of products at IBM Software. “Today’s release of IBM’s Granite model series and commitment to stand behind IBM-developed watsonx models is a testament to IBM’s end-to-end model lifecycle management process in its watsonx AI and data platform that delivers businesses cutting-edge AI outfitted for their unique business needs.”

IBM has developed foundational AI models that have been trained using datasets from five different domains: internet, academic, code, legal, and finance. These models have been carefully curated for business applications by IBM. The training data underwent a rigorous filtering process to remove objectionable content and was benchmarked against both internal and external models. 

According to IBM, this effort aims to ensure responsible deployment of AI and address issues like governance, risk assessment, privacy, and bias mitigation. IBM is utilizing its AI and data model lifecycle governance process to manage and mitigate client risk with the watsonx AI and data platform. Additionally, they plan to release watsonx.governance, an AI governance toolkit, later this year to facilitate trusted AI workflows.

IBM has also confirmed that its standard intellectual property protections will apply to these AI models.

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