Katonic AI to help Japanese businesses securely embrace the power of Generative AI

Previous technologies, such as templates and deep learning, required numerous training samples to comprehend lengthy medical documents or intricate legal documents. Katonic.ai, a startup born in Sydney, Australia, in 2020, after rapidly growing its customer base in APAC, is extending its expertise to Japanese customers with an end-to-end Generative AI solution for their business. The platform runs in a Kubernetes cluster and can be deployed anywhere, such as multi-cloud, on-premises, or the edge. “We understand that Japanese businesses need a reliable and secure platform to leverage the power of Generative AI. An AI-native organisation like Katonic, with roots in the region, can best understand the needs of businesses here,” said Prem Naraindas, CEO & Founder of Katonic.ai. This natural platform expansion has most recently manifested itself in embedded fintech solutions. VSaaS companies can monetize transaction volume by capitalizing on the benefits of integrated payments or offer tailored lending products where underlying access to customer data creates unique underwriting capabilities.

The alliance will produce a new IBM Consulting Azure OpenAI service offering, to help businesses strategise and define opportunities for the use of AI within their firm. Databricks’ unified Data and AI platform combined with MosaicML’s generative AI training capabilities will provide a platform robust enough to serve the world’s largest organisations and flexible enough to address a broad range of AI use cases. Google Cloud customers have the option to use AI models from Google itself as well as other companies, a degree of flexibility that appeals to startups, Lee said.

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Its success has led to significant investment, with Microsoft investing between 10 and 14 billion dollars. Timing of the launch hopes to capitalise on research revealed by the UK Government that just 15% of UK businesses have adopted at least one AI technology, such as ChatGPT or Bard. The new generative AI platform overcomes the limitations of data bias, lack of context and difficulty producing accurate personalisation by leveraging proprietary first-party data and external data currently unavailable in existing models like GPT-3 and GPT-4. Digital acceleration company Making Science has released its latest foray into the world of generative AI, launching a platform that integrates with OpenAI and Stable Diffusion to provide AI generative content validation and optimisation.

Google Cloud introduced its inaugural Accelerator program for gen AI startups, encompassing a 10-week series of technical workshops, mentorship, and leadership training. Data discovery for understanding key data drivers and behavioural groups, make insightful predictions and optimize decisions. The deployment of AI has entered uncharted territory as the technology and legal landscape both evolve. Regulating explicable – or “explainable” – AI models is completely different when it comes to AI models that cannot be explained or interpreted; the regulatory framework will only apply to their inputs and outputs.

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The ability to edit photographs quickly without any photo editing experience makes high-quality, bespoke imagery accessible to all. Things that were once complicated, like changing a background or adding special effects, are now simple to accomplish. This also has the potential to support social media marketing, with generative AI tools emerging that automatically brand your social media content. Leeway Hertz is a distinguished Generative AI development company and a software development firm specializing in providing bespoke digital solutions to businesses worldwide.

Most intellectual property frameworks, including the
UK’s, were not drafted with today’s AI technology in mind,
so we may well see IP laws revised in the near future to account
for the role AI will play in content creation. For now, the key
first step for those using AI in their everyday business or
personal lives will be to consider the T&Cs of the relevant AI
platform and to make sure you have all the rights necessary to use
the final output as you desire. Founded in 2015 by CEO, Anant Bhardwaj, the company provides a unified platform with artificial intelligence (AI) and workflow automation capabilities that allow organisations to solve their most pressing business challenges at scale. TOKYO, July 24, 2023 /PRNewswire/ — Katonic.ai, an Australian AI company, marked its entry into the Japanese market with the launch of its no-code Generative AI Platform in Japanese.

Founder of the DevEducation project

Together, let’s shape the future of technology and unlock new possibilities with generative AI. The synthetic data sets, generated using advanced generative AI techniques, mirror a company’s original customer data in detail but exclude the actual personal data points. Its global network of data centers ensures low latency and high availability for customers worldwide, making it a preferred choice for businesses looking to leverage generative AI and other advanced technologies. With over 3.3 million downloads of MPT-7B and the recent release of MPT-30B, MosaicML has showcased how organisations can quickly build and train their own state-of-the-art models using their data in a cost-effective way. Customers such as AI2 (Allen Institute for AI), Generally Intelligent, Hippocratic AI, Replit and Scatter Labs leverage MosaicML for a wide variety of generative AI use cases. Databricks, the Data and AI company, today announced it has entered into a definitive agreement to acquire MosaicML, a leading generative AI platform.

• The platform leverages AI algorithms to automatically generate code snippets and templates, assisting software developers in their coding tasks.
• Businesses optimism around the world rose significantly in early 2023, with the hype around generative AI leading many to invest in the technology with the expectation it would yield positive results in next to no time.
• Adobe’s suite of tools, including audio-visual content creation, editing, and publishing tools, are essential for the development of generative AI applications.
• This creates accountability concerns as the user does not know what source the AI has relied upon and therefore, cannot verify the accuracy of the information.
• The Synthetica Bio team has created a foundational partnership with one of the largest healthcare data providers in the industry.
• Boltzbit AI conducts new prediction tasks with fast fine-tuning and improves over time as more data is available.

Elemental Cognition CEO David Ferrucci says Google has also been in the AI space for so long, he trusts that the team will “talk a common language” when discussing his company’s needs, he said. It is also part of the information that we share to our content providers (“Contributors”) who contribute Content for free for your use. The Brandtech Group has made other investments in AI and AI-adjacent companies such as Crossing Minds, AI Foundation, CreativeX, VidMob, and its 2022 acquisition of Acorn-i, but this is its first acquisition specifically in the generative AI space and its 10th overall. Headquartered in San Francisco, Instabase is one of the most dynamic applied AI companies  emerge from the recent flurry of West Coast startups in the US. Katonic.ai has offices in India, Australia, and Singapore and is backed by Australia’s largest investment fund, Artesian Investments and Boab AI.

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Our aim is to make the Synthetica Bio platform the central destination for biopharma companies to rapidly and cost-effectively gain actionable insights into data from any source,” added Dr. Dickinson, Executive Chair of Synthetica Bio. Perplexity AI is an answer engine that uses large language models and search engines to answer complex questions accurately. It is designed to be a more robust and versatile alternative to traditional search engines and can be used to answer questions on a wide range of topics. For example, it can answer questions about historical events, scientific concepts, or even the meaning of life.

The first product in the portfolio, to be launched in Q2 2023, will pioneer the use of an AICI assistant that can be embedded within standard applications to support interactive decision-making for consumer businesses 24/7, and will leverage the OpenAI Large Language Model (LLM). Though the impact of generative AI is global, the leading camps for AI development have been concentrated in the US and China. US academic and industrial communities appear stronger in terms of original theories and AI infrastructure such as AI chips and developer frameworks. China’s advantage is its sizeable market for growing AI applications stemming from its large population and use of big data. “Historically, leadership in healthcare data platforms has been achieved by amassing the largest pools of data. We believe that LLMs are disrupting the traditional model and future leadership will be achieved by the platform that is best able to train and reason with these big data pools.

Can you afford the cost of insecure systems?

The relative speed and ease with which these tools can create
new content means companies across a wide range of industries are
relying on AI to help create content and deliverables. Last month,
Buzz feed announced it was planning to harness artificial
intelligence as part of its core business to personalize and
enhance its online quizzes and content (and that it would also be
cutting about 12% of its workforce to rein in costs). This is the second article in our AI 101 series, where the team
at Lewis Silkin, Ius Laboris’s UK firm, unravel the legal
issues involved in the development and use of AI text and image
generation tools.

MosaicML enables developers to maintain full control over the AI models they build with model ownership and data privacy built into the platform’s design. Since its founding, Instabase’s platform has had a rich history of incorporating AI innovation into its design, including content digitization, neural networks, and, most recently, large language models. Hence, the latest innovations in large language models represent yet another step-function in the ability to approach genrative ai unstructured data that we were able to quickly incorporate into our modular design once they were released. Large language models (LLMs) are capable of answering content-related questions without any fine-tuning, eliminating the need for laboriously annotating, training, or collecting training data. Consequently, we were able to take this technology and package it into the form factor of AI Hub, enabling anyone from an enterprise to a professional to access it.