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(This message was added in version 6.7.0.) in /home4/scienrds/scienceandnerds/wp-includes/functions.php on line 6114Source:https:\/\/techcrunch.com\/2023\/05\/09\/ibm-intros-a-slew-of-new-ai-services-including-generative-models\/<\/a><\/br> IBM, like pretty much every tech giant these days, is betting big on AI.<\/p>\n At its annual Think conference, the company announced IBM Watsonx, a new platform that delivers tools to build AI models and provide access to pretrained models for generating computer code, text and more.<\/p>\n It\u2019s a bit of a slap in the face to IBM\u2019s back-office managers, who just recently were told<\/a> that the company will pause hiring for roles it thinks could be replaced by AI in the coming years.<\/p>\n But IBM says the launch was motivated by the challenges many businesses still experience in deploying AI within the workplace. Thirty percent of business leaders responding to an IBM survey cite trust and transparency issues as barriers holding them back from adopting AI, while 42% cite privacy concerns \u2014 specifically around generative AI.<\/p>\n \u201cAI may not replace managers, but the managers that use AI will replace the managers that do not,\u201d Rob Thomas, chief commercial officer at IBM, said in a roundtable with reporters. \u201cIt really does change how people work.\u201d<\/p>\n Watsonx solves this, IBM asserts, by giving customers access to the toolset, infrastructure and consulting resources they need to create their own AI models or fine-tune and adapt available AI models on their own data. Using Watsonx.ai, which IBM describes in fluffy marketing language as an \u201centerprise studio for AI builders,\u201d users can also validate and deploy models as well as monitor models post-deployment, ostensibly consolidating their various workflows.<\/p>\n But wait, you might say, don\u2019t rivals like Google, Amazon and Microsoft already provide this or something fairly close to it? The short answer is yes. Amazon\u2019s comparable product is SageMaker Studio, while Google\u2019s is Vertex AI. On the Azure side, there\u2019s Azure AI Platform.<\/p>\n IBM makes the case, however, that Watsonx is the only<\/em> AI tooling platform in the market that provides a range of pretrained, developed-for-the-enterprise models and \u201ccost-effective infrastructure.\u201d<\/p>\n \u201cYou still need a very large organization and team to be able to bring [AI] innovation in a way that enterprises can consume,\u201d Dario Gil, SVP at IBM, told reporters during the roundtable. \u201cAnd that is a key element of the horizontal capability that IBM is bringing to the table.\u201d<\/p>\n That remains to be seen. In any case, IBM is offering seven pretrained models to businesses using Watsonx.ai, a few of which are open source. It\u2019s also partnering with Hugging Face, the AI startup, to include thousands of Hugging Face\u2013developed models, datasets and libraries. (For its part, IBM is pledging to contribute open source AI dev software to Hugging Face and make several of its in-house models accessible from Hugging Face\u2019s AI development platform.)<\/p>\n The three that the company is highlighting at Think are fm.model.code, which generates code; fm.model.NLP, a collection of large language models; and fm.model.geospatial, a model built on climate and remote sensing data from NASA. (Awkward naming scheme? You betcha.)<\/p>\n Similar to code-generating models like GitHub\u2019s Copilot<\/a>, fm.model.code lets a user give a command in natural language and then builds the corresponding coding workflow. Fm.model.NLP comprises text-generating models for specific and industry-relevant domains, like organic chemistry. And fm.model.geospatial makes predictions to help plan for changes in natural disaster patterns, biodiversity and land use, in addition to other geophysical processes.<\/p>\n These might not sound novel on their face. But IBM claims that the models are differentiated by a training dataset containing \u201cmultiple types of business data, including code, time-series data, tabular data and geospatial data and IT events data.\u201d We\u2019ll have to take its word for it.<\/p>\n \u201cWe allow an enterprise to use their own code to adapt [these] models to how they want to run their playbooks and their code,\u201d Arvind Krishna, the CEO of IBM, said in the roundtable. \u201cIt\u2019s for use cases where people want to have their own private instance, whether on a public cloud or on their own premises.\u201d<\/p>\n IBM is using the models itself, it says, across its suite of software products and services. For example, fm.model.code powers Watson Code Assistant, IBM\u2019s answer to Copilot, which allows developers to generate code using plain English prompts across programs including Red Hat\u2019s Ansible. As for fm.model.NLP, those models have been integrated with AIOps Insights, Watson Assistant and Watson Orchestrate \u2014 IBM\u2019s AIOps<\/a> toolkit, smart assistant<\/a> and workflow automation tech, respectively \u2014 to provide greater visibility into performance across IT environments, resolve IT incidents in a more expedient way and improve customer service experiences \u2014 or so IBM promises.<\/p>\n FM.model.geospatial, meanwhile, underpins IBM\u2019s EIS Builder Edition, a product that lets organizations create solutions addressing environmental risks.<\/p>\n Alongside Watsonx.ai, under the same Watsonx brand umbrella, IBM unveiled Watsonx.data, a \u201cfit-for-purpose\u201d data store designed for both governed data and AI workloads. Watsonx.data allows users to access data through a single point of entry while applying query engines, IBM says, plus governance, automation and integrations with an organization\u2019s existing databases and tools.<\/p>\n Complementing Watsonx.ai and Watsonx.data is Watsonx.governance, a toolkit that \u2014 in IBM\u2019s rather vague words \u2014 provides mechanisms to protect customer privacy, detect model bias and drift, and help organizations meet ethics standards.<\/p>\n In an announcement related to Watsonx, IBM showcased a new GPU offering in the IBM cloud optimized for compute-intensive workloads \u2014 specifically training and serving AI models.<\/p>\n The company also showed off the IBM Cloud Carbon Calculator, an \u201cAI-informed\u201d dashboard that enables customers to measure, track, manage and help report carbon emissions generated through their cloud usage. IBM says it was developed in collaboration with Intel, based on tech from IBM\u2019s research division, and can help visualize greenhouse gas emissions across workloads down to the cloud service level.<\/p>\n It could be said that both products, in addition to the new Watsonx suite, represent something of a doubling down on AI for IBM. The company recently built an AI-optimized supercomputer, known as Vela, in the cloud. And it has announced collaborations with companies such as Moderna and SAP Hana to investigate ways to apply generative AI at scale.<\/p>\n The company expects AI could add $16 trillion to the global economy by 2030 and that 30% of back-office tasks will by automated within the next five years.<\/p>\n \u201cWhen I think of classic back-office processes, not just customer care \u2014 whether it\u2019s doing procurement, whether it\u2019s elements of supply chain [management], whether it\u2019s elements of IT operations, or elements of cybersecurity \u2026 we see AI easily taking anywhere from 30% to 50% of that volume of tasks, and being able to do them with much better proficiency than even people can do them,\u201d Gil said.<\/p>\n Those might be optimistic (or pessimistic, if you\u2019re humanist-leaning) predictions, but Wall Street has historically rewarded the outlook. IBM\u2019s automation solutions \u2014 part of the company\u2019s software segment \u2014 grew revenue by 9% year over year in Q4 2022. Meanwhile, revenue from data and AI solutions, which focuses more on analytics, customer care and supply chain management, grew sales by 8%.<\/p>\n But as a piece in Seeking Alpha notes<\/a>, there\u2019s reason to lower expectations. IBM has a difficult history with AI, having been forced to sell its Watson Health division at a substantial loss after technical problems led high-profile customer partnerships to deteriorate. And rivalry in the AI space is intensifying; IBM faces competition not only from tech giants like Microsoft and Google but also from startups like Cohere<\/a> and Anthropic<\/a> that have\u00a0massive capital backing.<\/p>\n Will IBM\u2019s new apps, tools and services make a dent? IBM\u2019s hoping so. But we\u2019ll have to wait and see.<\/p>\n<\/p><\/div>\n <\/br><\/br><\/br><\/p>\n
\nIBM intros a slew of new AI services, including generative models<\/br>
\n2023-05-09 22:15:26<\/br><\/p>\nNew tools and infrastructure<\/h2>\n