• Atlassian Corporation Plc became the most recent technology company to incorporate generative artificial intelligence capabilities into its flagship collaborative software offerings.
• The ability of Atlassian Intelligence to translate natural language queries into Atlassian’s Jira Query Language is yet another interesting feature that should prove useful to developers.

Atlassian Corporation Plc is the latest technology business to include generative artificial intelligence capabilities in its core collaborative software solutions.

The new technology, Atlassian Intelligence, is partially based on the company’s models acquired through the January 2022 acquisition of Percept.AI. It also utilizes OpenAI LP’s GPT-4 model, notable for powering the ChatGPT chatbot, whose release late last year ignited a virtual AI arms race among major tech companies.

Dozens of software companies have attempted to capitalize on the hype surrounding generative AI, which enables machines to interact with humans and respond in a nearly realistic manner by answering queries, locating information, conducting tasks, and more.

Atlassian Intelligence was developed utilizing large language models at the core of generative AI and operates by constructing “teamwork graphs” that depict the various types of work being performed by teams and their relationships. Atlassian says its open platforms let it incorporate context from third-party apps.

GPT-4, which has been trained on vast amounts of publicly available online text, will be able to assist teams in multiple ways, according to Atlassian, including accelerating work, providing immediate assistance, and fostering a shared comprehension of projects.

In the Confluence collaboration platform, employees can click on any unfamiliar term within a document to receive an explanation and links to other relevant documents. Additionally, users can write queries into a chat area and receive automated responses based on the content of documents uploaded to Confluence. Tell it to generate a summary of a recent meeting and include a link to the transcript, and it will immediately spew out a list of agreed-upon decisions and action items.

In addition, Atlassian Intelligence can compose social media posts regarding a forthcoming product announcement based on the product’s Confluence specifications. Meanwhile, software developers utilizing Jira can swiftly compose a test plan based on the system’s operating system knowledge.

Users of Jira may also utilize a virtual agent that automates support via Slack and Teams. The agent would be able to retrieve information from existing knowledge base articles to assist both agents and end users and summarize previous interactions for newly assigned support agents so they are promptly brought up to speed on an issue.

The ability of Atlassian Intelligence to translate natural language queries into Atlassian’s Jira Query Language is yet another interesting feature that should prove helpful to developers.

Holger Mueller of Constellation Research Inc. said, “AI is coming to software development — that is unavoidable, and Atlassian doesn’t want to be left behind. It’s a good move, infusing ChatGPT capabilities into Confluence and Jira, because anything that increases the velocity of software developers will be welcomed by enterprises. What will be interesting to see is which of the new features become the most popular and useful.”

Atlassian stated that customers must sign up for a waiting list to access the new features currently available in beta testing mode for its cloud-based products. Some of the new features, such as the virtual agent for Jira Service Management, will be included at no additional cost in Atlassian’s Premium and Enterprise programs.

According to Atlassian, new users who sign up for the beta can anticipate seeing them in the coming months.

• Comet said that data scientists working on artificial intelligence for natural language processing no longer spend that much time training their models.
• Prompt Playground is one of the new tools that enables developers to iterate more quickly with various templates and comprehend how prompts affect various scenarios.

Comet ML Inc., a European machine learning operations startup, is adapting its MLOps platform to deal with large language models such as those that enable ChatGPT.

The startup announced adding many “cutting-edge” LLM operations features to its platform to assist development teams in expediting engineering, managing LLM workflows, and improving overall efficiency.

Comet, a startup founded in 2017, positions itself as accomplishing machine learning and artificial intelligence what GitHub accomplished for programming. Data scientists and engineers may automatically track their datasets, code modifications, experimental history, and production models using the company’s platform. Comet says this results in efficiency, transparency, and reproducibility due to its platform.

Comet said that data scientists working on artificial intelligence for natural language processing no longer spend that much time training their models. Instead, they spend much more time developing the appropriate instructions to address newer, more challenging challenges. Existing MLOps systems don’t have the tools necessary to monitor and analyze the performance of these prompts well enough, which is an issue for data scientists.

Gideon Mendels, Chief Executive of Comet, reported, “Since the release of ChatGPT, interest in generative AI has surged, leading to increased awareness of the inner workings of large language models. A crucial factor in the success of training such models is prompt engineering, which involves the careful crafting of effective prompts by data scientists to ensure that the model is trained on high-quality data that accurately reflects the underlying task.”

According to Mendels, prompt engineering is a method of natural language processing used to develop and perfect prompts necessary to elicit accurate responses from models. They are required to prevent “hallucinations,” which occur when AI creates responses.

The CEO stated, “As prompt engineering becomes increasingly complex, the need for robust MLOps practices becomes critical, and that is where Comet steps in. The new features built by Comet help streamline the machine learning lifecycle and ensure effective data management, respectively, resulting in more efficient and reliable AI solutions.”

Vice President and Principal Analyst at Constellation Research Inc. Andy Thurai said that because LLMs are still in the early stages of research, the majority of MLOps systems do not offer any tools for controlling workflows in that area. This is because LLM engineering entails modifying prompts for pre-trained models rather than training new models.

“The challenge is that, because LLMs are so big, the prompts need to be fine-tuned to get proper results. As a result, a huge market for prompt engineering has evolved, which involves experimenting and improving prompts that are inputted to LLMs. The inputs, outputs and the efficiency of these prompts need to be tracked for future analysis of why a certain prompt was chosen over others,” Thurai added.

Comet claimed that its new LLMOps tools are made to perform two tasks. One benefit is that they will speed up iteration for data scientists by giving them access to a playground for quick tuning integrated with experiment management. Additionally, they offer debugging capabilities that allow prompt chain visualization to trace prompt experimentation and decision-making.

Mendels said, “They address the problem of prompt engineering and chaining by providing users with the ability to leverage the latest advancements in prompt management and query models, helping teams to iterate quicker, identify performance bottlenecks, and visualize the internal state of the prompt chains.”

A prompt Playground is a new tool that enables developers to iterate more quickly with various templates and comprehend how prompts affect multiple scenarios. Prompt Usage Tracker, which teams may use to track their usage of prompts to understand their impact on a more granular level, is another debugging tool for prompts, responses, and chains.

Comet also disclosed new partnerships with LangChain Inc. and OpenAI LP, the company behind ChatGPT. According to the company, the OpenAI integration will make it feasible to use GPT-3 and other LLMs, while LangChain will make it easier to construct multilingual models.

“These integrations add significant value to users by empowering data scientists to leverage the full potential of OpenAI’s GPT-3 and enabling users to streamline their workflow and get the most out of their LLM development,” Mendels mentioned.

• With the help of AI agents, the new platform will be able to carry out trades on users’ behalf, ensuring the best possible trade outcomes and minimizing the need for manual interaction.
• A USD 40 million fundraising round led by DWF Labs was recently completed by the business in order to accelerate the creation of AI and autonomous agents.

Fetch.ai Ltd., an artificial intelligence laboratory based in Cambridge that develops AI-powered agents for peer-to-peer applications, has announced the development of new trading tools for decentralized cryptocurrency exchanges.

With the help of AI agents, the new platform will be able to carry out trades on users’ behalf, ensuring the best possible trade outcomes and minimizing the need for manual interaction. At the same time, autonomous agents may be programmed with user preferences and fine-tune tactics based on market circumstances, allowing users to communicate in a peer-to-peer fashion across marketplaces.

Decentralized exchanges are components of the more extensive decentralized finance (DeFi) economy, a token economy based on blockchain technology that enables direct peer-to-peer transactions between users. The business claims that this creates the potential for Fetch.ai’s machine learning algorithms to track market circumstances and link customers and sellers for optimum impact.

It follows that transactions occur with one-to-one smart contracts on the blockchain instead of big liquidity pools involving several trades and users because each seller and buyer is directly connected. Hackers and insider exit schemes known as “rugpulls,” in which the owner of the crypto wallet just takes all the tokens, are clearly after large pools of cryptocurrency tokens.

Humayun Sheikh, Chief Executive of Fetch.ai, said, “As we stand at the forefront of a new era in the DeFi sector, with rapidly evolving technologies and innovations, we recognize the need to go deeper into decentralization. AI agent-based trading has enormous potential to remove central points of failure and solve some of DeFi’s biggest problems such as liquidity contract hacks and rugpulls, which cost the industry billions of dollars a year.”

According to research by De.Fi Security, which monitors these trends, crypto protocols, and marketplaces, lost more than USD 452 million to scams and hacks during the first quarter of 2023. Despite the size of these figures, they are far less than the USD 1.3 billion in losses experienced during the same time period in 2022. Many of these crimes and losses result from vulnerabilities in cryptographic protocols and blockchain smart contracts.

The business recently completed a USD 40 million fundraising round led by DWF Labs to accelerate the development of AI and autonomous agents. Through the Amadeus global distribution system, Fetch.ai has already created autonomous AI travel agents that can link customers to more than 770,00 hotels globally and make reservations on their behalf. It also tested a smart parking space management program in Germany to balance the supply and demand for parking spaces.

The new tools from Fetch.ai will go on general sale in the second quarter of this year. According to the business, these products will be the first of their type to be sold.

• The venture intends to develop a succession of language models, the first of which is StableLM. Future installments in the series are expected to feature more intricate architectures.
• The new StableLM model from Stability AI can perform a comparable set of operations.

StableLM, an open-source language model that can create text and code, was recently released by Stability AI Ltd., an artificial intelligence business.

The venture intends to develop a succession of language models, the first of which is StableLM. Future additions in the series are expected to feature more intricate architectures.

Stability AI, based in London, is supported by USD 101 million in funding. It is best known as the creator of the open-source neural network Stable Diffusion, which can generate images based on text input. A few days before the latest introduction of the StableLM language model, the startup released a significant update to Stable Diffusion.

StableLM is initially available in two versions. The first consists of three billion parameters and the configuration settings determining how a neural network processes data. The second version contains seven billion of these settings.

The more parameters a neural network has, the more tasks it can complete. PaLM, a large language model described by Google LLC last year, is configurable with over 500 billion parameters. It has demonstrated the ability to generate code and text and solve relatively complex mathematical problems.

The new StableLM model from Stability AI can perform comparable operations. However, the startup still needs to disclose specific information regarding the model’s capabilities. Later on, Stability AI intends to publish a technical overview of StableLM.

While the startup did not reveal specific information about StableLM, it did reveal how the model was trained. Stability AI created it using an enhanced version of The Pile, an open-source training dataset. The standard edition of the dataset contains 1.5 trillion tokens, data elements consisting of a few letters each.

StableLM is licensed under the CC BY-SA 4.0 open-source license. The model can be used in research and commercial endeavors, and its code can be modified as needed.

Stability AI stated in a blog post, “We open-source our models to promote transparency and foster trust. Researchers can ‘look under the hood’ to verify performance, work on interpretability techniques, identify potential risks, and help develop safeguards. Organizations across the public and private sectors can adapt (‘fine-tune’) these open-source models for their own applications.”

Stability AI released five StableLM variations trained on datasets other than The Pile. Training a model of artificial intelligence on additional data enables it to incorporate more information into its responses and perform new tasks. The five specialized variants of StableLM might be restricted to use in academic research.

Dolly, a collection of 15,000 chatbot queries and replies, was among the datasets Stability AI used to train the specialized variants of StableLM. Databricks Inc. released Dolly earlier this month. The dataset was used by Databricks to train an advanced language model available under an open-source license, similar to StableLM.

StableLM is in the alpha phase. This is the first language model that Stability AI intends to disclose. The startup plans to create StableLM variants with 15 billion to 65 billion parameters as part of its development plan.

• The business asserts to have secured over USD 2 billion in bitcoin assets spread across tens of thousands of keys globally.
• Unchained Capital uses a “collaborative custody” technique to store bitcoins for users.

Unchained Capital Inc., based in Austin, Texas, revealed recently that it had raised USD 60 million in new Capital to expand its financial services for bitcoin owners amid volatile markets.

Leading the Series B funding round was Valour Equity Partners, which is well recognized for its early investments in Tesla and SpaceX. Existing investors such as NYDIG, Trammell Venture Partners, Ecliptic Capital, and Highland Capital Partners took part in the round’s early April closure. It adds to the USD 15 million the company raised in the fall of last year under Ten31’s leadership.

Unchained Capital uses a “collaborative custody” technique to store bitcoins for users. The company’s solution uses the bitcoin’s underpinning technology, which enables numerous parties to keep track of private keys and distribute ownership of bitcoins among themselves, Unchained, or other financial institutions.

The technology implements a multi-signature concept that, in essence, necessitates the agreement of multiple parties before the transfer of cryptocurrency. Unchained, and any other financial institution, cannot act on held cryptocurrency without the consent of the other signatory. This helps eradicate risks such as theft and critical loss and prevents anything from occurring to holdings if one signatory passes away.

It has been demonstrated that holding cryptocurrencies with businesses might result in the loss of tokens by the recent collapse and bankruptcy of cryptocurrency exchange FTX Trading Ltd. and other centralized crypto enterprises. The possibility that they may be transferred or used without permission—or that someone would just run off with them—reduces, though, if many people sign them.

The company asserts to have secured over USD 2 billion in Bitcoin assets spread across thousands of keys globally. It offers loans with Bitcoin as collateral among its financial services, and since 2017 it has originated more than USD 500 million in loans with no losses. The company also provides a trading desk for customers to purchase Bitcoin directly into multi-signature cold storage. It also has a solution for individual retirement accounts, enabling customers to hold Bitcoin keys in tax-advantaged accounts.

A partner at Valor, Vivek Pattipati, said “In the midst of market chaos, Unchained has emerged as a highly trusted provider of bitcoin custody and financial services through superior technology, risk management, regulatory compliance, and client service. Particularly in lending, the company has differentiated itself by minimizing risk to both the lender and the borrower, leading to resilience and an extraordinary opportunity to capture market share.”

Given the current market volatility, unchained intends to utilize the money to increase its clientele and broaden its product selection, particularly in relation to Bitcoin. According to Chief Executive Joe Kelly, the cash will be used to expand the product line and address long-requested additions, upgrades, security features, and quality-of-life improvements.

With an eye on how people exchange Bitcoin for dollars, a portion of the funds will also be used to capitalize on the expanding popularity of Bitcoin. Unchained Capital plans to start providing dollar-focused products like checking accounts and credit cards in light of this.

Kelly said, “Using this fresh capital investment to expand our reach and suite of services. We hope to enable new entrants to bitcoin to leapfrog centralized custodians into our safer collaborative custody model.”

• Users can upload template characters from any game to make sure their new avatars reflect the game’s look to maintain consistent designs.
• The AI-generated game characters from Popul8 are believed to work with other design tools like Mixamo, ARKit, and Amazon Polly, as well as popular graphics engines like Unity and Unreal Engine.

Didimo Inc., a producer of digital human avatar technology, is introducing generative AI to create video game characters.

Didimo claims that with the release of Popul8, game developers now have an easy way to produce hundreds of distinctive gaming characters in a small fraction of the usual time and expense. Popul8 is a generative AI tool that expands on Didimo’s current avatar generation toolkit, reducing the time it takes to produce incredibly lifelike, 3D video game, and metaverse characters with complete control over their look.

Traditionally, creating realistic-looking characters for video games required endless hours, with painstaking attention to detail being an essential step in the design process. Designing animated and varied avatars in a matter of minutes is now possible with Popul8.

Users can upload template characters from any game to make sure their new avatars reflect the game’s look to maintain consistent designs. The appearance of each figure can then be adjusted by artists. Additionally, Popul8 supports “batch creation,” which allows creators to produce a large number of unique characters at once, each with their own random attributes, to fill out entire worlds or levels with non-playable characters.

Sean Cooper, Client Integration Lead at Didimo, said, “By streamlining the character creation process, we’re unlocking the ability for artists to create richer worlds, with character diversity, at a fraction of time and necessary technical resources.”

The AI-generated game characters from Popul8 are believed to work with other design tools like Mixamo, ARKit, and Amazon Polly, as well as popular graphics engines like Unity and Unreal Engine. Additionally, they are memory-optimized, allowing game designers to include hundreds of lifelike characters without having to sacrifice their “memory budget” or the performance of the game.

Holger Mueller, Analyst at Constellation Research Inc. reported that generating video game characters is time-demanding and tiresome. “Can you remember the days when game studios put sensors on humans to capture their motion and replicate it in video games? It was expensive and generative AI can therefore potentially disrupt the entire industry by lowering the cost. It can democratize game creation by empowering many more creators,” he added.

Veronica Orvalho, Chief Executive and Founder of Didimo, mentioned that eliminating the cumbersome task of character development is the benefit of Popul8, thereby enabling developers to emphasize on the real gameplay. “With our continuing focus to make digital spaces available to everyone by allowing more diverse, representative, authentic and personal engagement, this platform is setting the standard for positive uses of AI generation,” she added.

Colossal Order Ltd., Sony Group Corp., Atom Stars, Soleil Ltd., Ceek VR Inc., NOS Communications Inc., and such game developers are already utilizing Popul8, which can now be accessed by everyone.

• Users can upload template characters from any game to make sure their new avatars reflect the game’s look to maintain consistent designs.
• The AI-generated game characters from Popul8 are believed to work with other design tools like Mixamo, ARKit, and Amazon Polly, as well as popular graphics engines like Unity and Unreal Engine.

Didimo Inc., a producer of digital human avatar technology, is introducing generative AI to create video game characters.

Didimo claims that with the release of Popul8, game developers now have an easy way to produce hundreds of distinctive gaming characters in a small fraction of the usual time and expense. Popul8 is a generative AI tool that expands on Didimo’s current avatar generation toolkit, reducing the time it takes to produce incredibly lifelike, 3D video game, and metaverse characters with complete control over their look.

Traditionally, creating realistic-looking characters for video games required endless hours, with painstaking attention to detail being an essential step in the design process. Designing animated and varied avatars in a matter of minutes is now possible with Popul8.

Users can upload template characters from any game to make sure their new avatars reflect the game’s look to maintain consistent designs. The appearance of each figure can then be adjusted by artists. Additionally, Popul8 supports “batch creation,” which allows creators to produce a large number of unique characters at once, each with their own random attributes, to fill out entire worlds or levels with non-playable characters.

Sean Cooper, Client Integration Lead at Didimo, said, “By streamlining the character creation process, we’re unlocking the ability for artists to create richer worlds, with character diversity, at a fraction of time and necessary technical resources.”

The AI-generated game characters from Popul8 are believed to work with other design tools like Mixamo, ARKit, and Amazon Polly, as well as popular graphics engines like Unity and Unreal Engine. Additionally, they are memory-optimized, allowing game designers to include hundreds of lifelike characters without having to sacrifice their “memory budget” or the performance of the game.

Holger Mueller, Analyst at Constellation Research Inc. reported that generating video game characters is time-demanding and tiresome. “Can you remember the days when game studios put sensors on humans to capture their motion and replicate it in video games? It was expensive and generative AI can therefore potentially disrupt the entire industry by lowering the cost. It can democratize game creation by empowering many more creators,” he added.

Veronica Orvalho, Chief Executive and Founder of Didimo, mentioned that eliminating the cumbersome task of character development is the benefit of Popul8, thereby enabling developers to emphasize on the real gameplay. “With our continuing focus to make digital spaces available to everyone by allowing more diverse, representative, authentic and personal engagement, this platform is setting the standard for positive uses of AI generation,” she added.

Colossal Order Ltd., Sony Group Corp., Atom Stars, Soleil Ltd., Ceek VR Inc., NOS Communications Inc., and such game developers are already utilizing Popul8, which can now be accessed by everyone.

• According to The Times, Google’s project needs a clearer timeline, making it unclear when the features might go live.
• The Times reports that Google plans to debut Magi next month before adding more new features in the following months.

When its core business is under the most serious threat in years, Google LLC is reportedly rushing to introduce new features and capabilities in its search engine powered by artificial intelligence.

According to reports, the company is developing a brand-new, AI-powered search engine and is considering updating its current search technology with AI features.

The changes are Google’s response to Samsung Electronics Co. Ltd.’s suggestion that it might stop using Google Search and switch to Microsoft Bing as its default mobile search engine, the New York Times reported recently.

According to The Times, Google could lose Samsung and suffer a loss of revenue of more than USD 3 billion annually. The suggestion allegedly caused widespread “panic” within Google; as a result, forcing the company to scramble to keep up with the surge in demand for technologies like ChatGPT.

As details obtained by the Times from internal emails, Google’s response is to update its search engine as part of a project called “Magi.” According to reports, Google has 160 employees working in “sprint rooms” to develop new AI-powered Google Search features.

Since December of last year, when executives first understood the significance of OpenAI LP’s ChatGPT and how it might present a problem for search, Google is said to have been in a frenzy. When Microsoft Corp. revealed plans to integrate ChatGPT with Bing in February, the threat to Google’s decades-long search market dominance only grew. Sundar Pichai, the CEO of Google, responded by pledging soon to update Google Search with new AI chat features.

A new service that will try to predict what users are looking for before they search is one of the new features Google is developing as part of a “more personalized” experience. According to The Times, Google’s project needs a clearer timeline, making it unclear when the features might go live.

A Chrome feature called “Searchalong” that would scan the website the user is reading and provide contextual information is among the other new features rumored to be in the works. The business is also developing a chatbot that can provide code snippets in response to questions about software engineering. A second chatbot would aid in music discovery. More experimental features, including “GIFI” and “Tivoli Tuto,” are also being developed, allowing users to ask Google Image Search to create images and communicate with a chatbot in a different language.

However, it should be noted that many of these features are only partially original. For instance, There is an existing image generation function in Slides, and Tivoli Tutor sounds a lot like Duolingo Inc.’s learning app.

Google’s apparent panic and haste to enhance the capabilities of its search engine, according to analyst Charles King of Pund-IT Inc., shows how flawed the ad-based search model has grown. He said, “Once upon a time, a search engine’s value was based on the quality of results it delivered, but today it’s likely that the top five or ten results you see for any given search will consist of sponsored ad links from some commercial entity.”

As a result, all internet users could gain from improved search capabilities. King said he would be surprised if Google couldn’t produce new AI-based tools that are at least on par with Microsoft’s, if not superior to them.

King said, “That said, the history of the tech industry is littered with stories of once-unstoppable firms that were undermined by more nimble and advanced competitors. Remember when Microsoft Explorer dominated the browser market to the point that the company was successfully challenged on anti-trust grounds? Then along came Google Chrome. Maybe this is just the latest tale of ‘what goes around, comes around'”.

According to Constellation Research Inc.’s Holger Mueller, who is more upbeat, Google’s plan to create a brand-new search engine based on generative AI makes sense because incremental innovation may only go so far in developing next-generation search. The analyst said, “At the same time, the coming reported updates are a good move as they can hedge against Microsoft Bing’s new AI capabilities. Though Google will in any case need to be cautious, as the verdict is still out on whether or not generative AI can really improve search experiences.”

According to The Times, Google plans on introducing Magi in the coming month before following up with more features in the fall. According to this timeline, more information about Magi might be made public on May 10 during Google I/O 2023. According to reports, Google intends to first make Magi’s features available to 1 million test subjects before making them available to 30 million users by the end of the year. Magi will initially only be made available in the United States.

Google declined to respond to the Times’ claims in a statement directly but claimed that it has been incorporating AI capabilities into Google Search for years through features like Lens and multisearch, among others.

A Google spokesperson said, “We’ve done so in a responsible and helpful way that maintains the high bar we set for delivering quality information. Not every brainstorm deck or product idea leads to a launch, but as we’ve said before, we’re excited about bringing new AI-powered features to Search, and will share more details soon.”

• According to the company based in Mountain View, California, Capitola’s platform increases the productivity of brokerage firm employees by eliminating repetitive manual tasks.
• The platform employs AI to monitor the insurance-related risks in terms of the company’s assets.

Capitola Insurance Services LLC, a startup that uses artificial intelligence to simplify commercial insurance transactions, has raised USD 15.6 million in funding.

Munich Re Ventures led the Series A round, which was announced recently. Lightspeed Venture Partners, a previous Capitola investor, also took part. In 2021, the year it was founded, the startup raised a five-million-dollar seed round.

Businesses do not purchase insurance directly from insurers but rather through a broker, an intermediary. The broker assists in identifying a policy that will meet the company’s needs. It then locates an insurer willing to take on that policy.

According to the company based in Mountain View, California, Capitola’s platform provides a software platform that helps simplify commercial insurance brokers’ lives. According to the company, its platform increases the productivity of brokerage firm employees by eliminating repetitive manual tasks.

Businesses frequently purchase insurance for multiple assets, such as servers and manufacturing equipment. Each of these items poses a different level of danger. A commercial insurance provider will only cover an asset if the risk level of that asset is aligning with its internal policies.

One of the most difficult aspects of insurance brokers’ work is finding an insurer willing to cover a company’s assets. Capitola claims that its platform can make the job easier. The platform employs AI to monitor the insurance-related risks in terms of the company’s assets. Then it matches those risks with insurers willing to underwrite policies at that risk level.

When deciding on an insurer, a broker considers several factors. Capitola claims that its platform provides data that enables brokers to perform such evaluations more quickly.

A commercial insurance policy has an attachment point specifying the maximum loss the insurance company will cover. This metric influences the monthly fee that a business must pay for its insurance policy. Capitola’s platform includes an analytics tool that enables users to compare between different attachment points and their associated monthly payments to find the best combination.

It also offers access to other data, such as how much a broker can make from selling a particular insurance policy. The data is visualized in charts by the company to facilitate user analysis.

Capitola claims that its platform can assist brokers in selling new policies and renewing existing ones. When a policy is about to expire, it alerts brokers and allows them to request a renewal from the insurer underwriting that policy. It also includes features that allow you to switch to a new insurance provider if necessary.

Capitola’s platform also includes additional productivity features. It includes a task assignment tool that allows managers to coordinate who oversees which aspects of a commercial insurance transaction. The employees of the broker’s office can generate a proposal automatically, when a business plans to buy a fresh policy or renew an existing one.

Co-founder and CEO Sivan Iram said, “The insurance industry has seen many technological advancements over the years, but very little attention has been given to the insurance professionals and the tools they use. Our platform brings together brokers and underwriters, removing many of the operational inefficiencies around manual processes and repetitive tasks to allow them to focus on what they do best.”

Capitola plans to use a part of the recent USD 15.6 million round to expand its presence in the United States. It also intends to add new features, such as market intelligence tools for brokers.

• Massive advances in AI technology are being driven by the emergence of accelerated computing, which is forcing businesses across industries to rethink their products and business models fundamentally.
• According to Deloitte, generative AI has opened up a wide range of new market applications and can significantly increase business productivity.

Deloitte Touche Tohmatsu Ltd., an information technology consulting company, announced recently that it is launching a new practice to assist businesses in leveraging generative artificial intelligence, the hottest trend in the sector.

Because it can create new content based on brief text descriptions, this trend has recently taken over the news. Generative AI is powered by a new generation of chatbots and virtual assistants, most notably OpenAI LP’s ChatGPT, which can converse, create artwork, write code, and more.

Businesses are eager to adopt the generative AI trend and learn how the technology can improve their operational efficiency and financial performance. Still, with so much discussion surrounding the subject, figuring out where to begin can take time. Deloitte believes it can assist in this area by giving enterprise leaders the deep AI industry experience they require to develop their generative AI strategies.

According to Deloitte, generative AI has opened up a slew of new industry applications and has the potential to greatly boost company efficiency. While developing, implementing, and operationalizing new applications based on fundamental AI models, many people require assistance.

The new practice at Deloitte will be dedicated to assisting businesses in implementing both custom-built solutions and those provided by third parties. The Generative AI Market Incubator, a group of engineers devoted to fast designing and launching generative AI pilot applications, is one of its major components.

In order to train and improve foundation models, Deloitte also established a research and development team that will collaborate with its alliance partners, the company said. Due to Deloitte’s early adoption of generative AI and its acquisitions of startups like HashedIn Technologies and Intellify Inc., both teams are said to have extensive experience in AI and the cloud.

Additionally, the new practice will collaborate with the Deloitte AI Academy. Its purpose is to train thousands of people in various new AI skills, including model development and prompt engineering, and to close the talent gap in AI.

Deloitte cited its most recent AI Dossier report, which outlines some situations in which generative AI can be used to good effect almost immediately. They include, among other things, supply chain optimization, fraud detection, and smart factories. With the help of its new practice, clients will be helped as they deploy applications in these fields and as they navigate ethical, legal, and policy issues.

Holger Mueller, an analyst with Constellation Research Inc., argued that Deloitte made a wise decision by taking this action because businesses will require assistance in implementing and maintaining the most recent AI tools.

Mueller added, “Of course, that help is going to come from the traditional system integrators like Deloitte, which is starting with an incubator. It is land grab time for the AI services category as many things are in flux.”

The cliche that “generative AI is transforming the way we work” was echoed by Jason Girzadas, managing principal of businesses, global, and strategic services at Deloitte U.S. and the firm’s incoming Chief Executive. Deloitte is prepared to assist its clients as they “develop and deploy new and innovative AI-fueled solutions,” he said, as businesses look to adopt the trend.