? AIOps replaces observability as this year’s hot new buzzword among IT ops teams.
? A new breed of AIOps platforms will hit the market in the next six to 12 months.
New comprehensive app platforms will gain significant importance, designed to be used in scaled-out and computationally complex Kubernetes environments. Advanced analytics of data will address IT operations issues including performance monitoring, troubleshooting unknown issues, capacity planning, and cost optimization. In short order, users of automation platforms will have access to generative AI (GenAI) capabilities for use of natural ausgedehntuage inputs to quickly create workflow scripts.
? Snowflake showed off generative AI (GenAI) enhancements to its platform including enabling workloads to be run natively without data egress and a range of partner LLMs offered directly on the platform.
? While Snowflake has been aggressive in adding tools and features to support GenAI, it remains in many ways a closed ecosystem competing against larger rivals for the fast-growing market.
At the Data Cloud World Tour Sydney, held this August, Snowflake laid out updates and enhancements to its cloud-based data management and analytics platform. Further, it demonstrated its momentum in the Asia-Pacific region, bringing together local customers and partners to showcase its capabilities and efforts in Australia and the wider region. Snowflake’s core proposition remains providing a cloud-based “single source of truth” for data across an enterprise. By migrating all data and related workflows onto the platform Snowflake aims to eliminate silos, reduce costs related to data transfer and copying, and improve security and governance by creating one perimeter. This remains the core value proposition for the platform, as the company continues to expand capabilities and services beyond its roots in data warehousing, to offer a greater range of related data and analytics services. The event was used to showcase updates made to the platform in the last year through internal development as well as via acquisition as the company continues to expand the scope of what is one offer. One of the most central themes was the company’s effort to tackle the emerging market for GenAI services.
? Meta announced general availability of Llama 2, the next generation of its open source LLM, which is free for research and commercial use.
? Meta is challschmaling the market dominance of OpenAI, with a radically different business model based on the open-source availability of its technology.
Meta recently released Llama 2, the latest version of its open-source large ausgedehntuage model (LLM), which uses artificial intelligence (AI) to generate text, images, and code. Llama was first released in February 2023 as a collection of foundation models, and it was made available exclusively to researchers. Now, Meta has just released the commercial version of the LLM, which will enable developers and businesses of all sizes to build applications. Because the technology is open source, access to Llama gives anyone the opportunity to improve the AI, accelerating technological innovation. By contrast, OpenAI’s GPT-4 is a so-called ‘black box’ in which the data and code used to build the model are not available to third parties.
? Automation Anywhere extends its Google partnership to inject GenAI features within Automation Success Platform, powered by Google’s LLM via Vertex AI.
? The new GlobalData GenAI Watch Newsletter recaps similar announcements by Oracle and Salesforce.
Application platform providers continue to compete via generative AI (GenAI) advantages, granting greater access to advanced innovations typically reserved for the most elite data experts. This week, intelligent automation leader Automation Anywhere reiterated its GenAI strategy, following similar news of GenAI-injected developer platforms by Oracle, Salesforce, and Google.
? Without a transparent commitment to data integrity, tech companies – and, increasingly, all businesses – will struggle to retain customer trust.
? ‘Digital trust’ goes beyond customer personal data protection, however, extending to trust in fundamental data integrity in all digital interactions.
As just about every tech company embraces artificial intelligence (AI), machine learning (ML), and data analytics to take advantage of the long-term trend of digital transformation,?digital trust?has emerged as a key issue for consumers and enterprises – and for the tech companies themselves. The idea is that today’s increasingly data-centric world is only possible with transparency and trust, and that trust and security in digital business models is a fundamental requirement, and not optional.
? There are many potential use cases in the enterprise for generative AI, but many will be enabled by existing cloud solutions.
? Some use cases requiring real-time responses may emerge, generating modest demand for MEC and/or 5G services.
Expectations of demand for 5G and multi-access edge computing (MEC) services from the enterprise segment are established – in part – on enabling artificial intelligence (AI) to be used in real-time applications. AI requires considerable computing power, usually achieved in the cloud where its demanding requirements can be scaled, but where such resources are too distant (due to network latency) to be relied upon for use cases where seconds or milliseconds in application response time can determine success or failure. There are other reasons why MEC makes sense in this scenario, including both the security benefits and cost savings achieved by not sending massive amounts of data to and from the cloud. With the recent hype around generative AI and the potential impact on various professions, industries, and organizations, it is worth considering whether its uptake will mean even more demand for MEC and/or 5G.
? The telecom wholesale segment is behind the AI implementation curve, and companies need to do more by embracing innovation – i.e., exploiting opportunities for generative AI.
? Success for telecom wholesalers will entail developing an underlying AI strategy across the portfolio and company in a connected manner.
Telecom Wholesale Trends in 2023 GlobalData’s discussions over the last year with global leading telecom wholesale providers highlights a commonality in strategy among telecom providers selling wholesale connectivity, both in terms of strategic vision and in the products and services they offer. Where companies differ is influenced by the nature of the core networks that support products and services, global geographical reach, and strschmbetagth of product/service brand (e.g., antifraud solutions, mobile roaming), and lastly partnerships.
? Since customer databases are available for mass business markets alongside providers’ existing major enterprise knowledge, service providers have traditionally segmented target markets by number of employees.
? Service providers are realizing they need to be more sophisticated and are trying to identify factors like digital maturity and proportion of knowledge workers.
More often than not, enterprise telecoms service providers segment the market in terms of employee numbers. Typically, they divide the market into SOHO/micro (0-5 employees: owner-managers don’t count as employees), SME/SMB (from 6-250 employees), and corporate/enterprise (250+ employees). Of course, these definitions vary from one service provider to the next, and often, specialist markets such as the MNC segment and public sector are addressed outside of the employee count model. The main drivers behind this are: (1) ‘this is how we’ve always done it,’ (2) ‘we can get databases of the target market by employee numbers,’ (3) and ‘any other approach is too difficult.’
? Tech buzzwords work when they successfully communicate innovation in a catchy phrase.
? The emerging ‘AIoT’ construction is awkward, but it may help IoT providers communicate their value to knowledgeable tech audiences.
The concept of combining AI and IoT has been around for a few years. More recently, some tech market players have begun using the phraseology ‘AIoT’ to capture it. A good technology buzzword helps communicate instantly to tech and non-tech audiences what the innovation is all about, or at least provides a sizable hint. Both ‘artificial intelligence’ and ‘Internet of Things’ have been pretty good at this, but the mashup term AIoT (or ‘artificial intelligence of things’) is awkward, not self-explanatory, and ultimately, unhelpful.
? The Italian National Authority for Data Protection has temporarily banned ChatGPT after a leak exposed the personal data of users of the paid-for version of the service.
? OpenAI, the company behind the popular chatbot, has 20 days to respond to the privacy watchdog or risks a fine equivalent to 4% of its annual turnover.
The Italian National Authority for Data Protection became the first regulator to start an investigation into OpenAI’s ChatGPT last week. Debate turned to the inevitability of increased regulatory oversight to control the effects of the explosion in the use of generative AI, and the possibility that the measure could be followed by other Western democracies, as the conversation around AI and ethics becomes more urgent.