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How are Artificial Intelligence (AI) Approaches Being Used to Process and Modify Diamonds?

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Artificial intelligence has helped evolve various fields, one of them is the diamond industry. The mentioned industry has gone through various phases as technologies and expertise have continued to develop. The recent few years have brought immense changes to diamonds’ excavation, manufacturing, and modification.

Diamonds are considered of high monetary value, which is why they have attracted various techniques regarding their manufacture. The continuing market value of diamonds has boosted the research regarding the use of AI in this field. 

These ideas and approaches have helped bring change from the process of extraction to the complete finishing. It has made the job easier for the manufacturers and added refinement for the customers. Here is a brief overview of the impacts of the AI industry on the diamond industry and how AI approaches are being used to modify diamonds.

AI and Changing Diamond Industry

The change in the diamond industry regarding AI began early in the 1990s and 2000s. It was when some Israeli manufacturers employed some modern technological techniques in the diamond industry. They realized that the most important part of the process was planning and deciding the process. The same plan is used to develop each stone into a refined diamond. Since then, it has continued, and the mentioned industry continues to use AI.

If a plan is programmed using AI for this purpose, it will enhance the stone and make it easier for the manufacturers. AI comes to help enhance the process as it is a collection of multiple programs that consider every aspect of the given job. It is perfect for diamonds as their manufacturing process requires hundreds of considerations. Thus, it takes care of all these to get the manufacturer the perfect piece.

AI can also be employed in the preparation of lab-created diamonds as it uses various programs that can generate an ideal diamond. The uses of AI are not just limited to the use in the mining and polishing process. Rather it helps businesses to keep record of their products and arrange it according to their needs. The huge databases that AI uses brings ease in identification, management, and ease for customers.

As diamond is a valuable commodity, it has required special programs that can cover the process from mining to shipping. Also, the available software can also be used to identify if the real diamond has been exchanged for fake during the shipping process. RockHer is an app that utilizes IBM’s technology for the identification of diamonds. 

Uses of AI in the Diamond Industry and Diamond Modification

There are various uses of AI in the diamond industry, and it can be used in the modification of diamonds. Some of these are given as follows:

1. Grading and Analysis

AI enhances the process of diamond grading and analysis using its advanced programs. In contrast to AI, it would cost too much time and energy if it is done traditionally. AI tools ensure that real-time data for a diamond is taken and it is analyzed quickly. Though the process for AI grading of diamonds is not new, it is improving constantly. 

It simplifies the diamond planning and drilling process as the decisions are data-driven. It employs scanning and imaging technologies while also bringing forth the use of the latest available software for grading. Thus, it reduces the human effort to the minimum possible, and the use of extensive machinery for grading and analysis is reduced.  

It fulfills the aim of creating an attractive diamond that requires minimum human effort. It brings more revenue to the business and reduces costs.

2. Inventory

The business of a brand depends on an efficient inventory. It can bring the best to the market and serve the customers with the products that the customers demand the most. If AI is used for diamonds, it can improve the standards with the help of AI programs. 

The manufacturer can apply specific grading standards that the customers demand the most, and it will improve business.The availability of AI increases accessibility and thus improving the availability of products. 

Another use is that of high accuracy in manufacture that is available in the case of AI, which is not always possible in the case of human beings. The manufacturers can follow market suitability according to popular demand and create diamonds with the required attributes. 

The AI-based manufacturing process is flexible and can be used for various procedures. It reduces effort while not affecting the inventory, keeping the business afloat.

3. Improving Diamond Pipeline

The whole process of diamond manufacturing is too complex as it involves various steps from mining to analysis and sale. It is through the use of AI programs that the process is streamlined from the start to the end. The use of these programs reduces the cost of the procedures and makes the process easier. The availability of AI tools makes it possible to get reliable results once, instead of requiring multiple checks.

The job of artificial intelligence starts from the mining process. It is through geological tracking devices and other tools that identify the quality of diamonds. In the case of manual exploration or the use of some other tools, there are chances of being misled by wrong information. The use of AI reduces the chances of error, and it helps in the complete process.

Miners get data through drilling rigs and estimate the costs and the benefits a diamond would bring the investors. Then the diamond is processed using other AI tools required for the remaining process.  

4. Using AI for accuracy

AI is considered a reliable tool from mining to grading process. It streamlines the process as it bases the procedure of analysis, grading, or any other purpose on a reliable set of data. It brings accuracy to the results which is far better compared to human-checked data. 

Another factor that makes it far better is the constant improvement in AI while in comparison, improvement in human accuracy is relatively slower. The developers of programs can improve their algorithms as they can add further data related to diamonds. 

Thus, it won’t be too long when AI-based grading and other processes would be the most-reliable ones for businesses. 

Some AI Systems and Technologies Used in Diamond Industry

Various AI systems and technologies have been developed by experts, which have found wide use in the diamond industry. Some of the popular names include DiaCam360, Ringo, Sarine e-Grading, etc. These computer programs have developed through trial and error, thus giving the best possible results to the users. 

DiaCam360 is an Israel-based developers’ venture who prepared this tool for the analysis of the color and clarity of the diamond. It is based on various databases which contain diamond pictures and other details. Its databases are GIA-affiliated, giving it credibility.

Ringo is an AI-based tool that can help find customers a diamond according to their specific choice. The choice might include color, shape, budget, etc. Sarine e-Grading is a tool that is being used for grading based on 4C’s. It gives object results that can be relied on.   

As AI has continued to develop, the mining process has simplified with the reduced costs. There are AI tools that help identify various factors like groundwater, subterranean ventilation, temperature etc. to see if it would be feasible to mine diamonds. Thus, it reduces the risk facto to ensure that the process is safe for miners. 

Tracking systems, wireless devices, detection tools, etc. are connected with AI tools to make mining safer and easier. Also, use of AI helps the miners analyze satellite images to see the environmental impact of mining.

Machine learning has helped in predicting dangers in a timely manner. It keeps the mining persons safe by analyzing data and considering if there would be spike in pumps. These also consider the formation of sludge deposits and ore fragmentation. 

Thus, the miner is easily able to mine diamonds, reducing the chances of any untoward happening. Robots using AI have found multiple uses in diamond mines. An example is Western Australia where such robots are being used for load, haul, and dump purposes in diamond mines. These are automated machines utilizing AI for their job.    

GIA first started testing use of AI for diamond clarity grading in 2020 partnering with IBM.  They aim to expand it further to other domains as well. 

Conclusion

The development of artificial intelligence (AI) has brought human beings some unparalleled comforts. The diamond industry also benefits from the developments in these fields and has brought programs and tools that help the process from manufacture to refinement and sale. These tools make it easier to determine quality, modify the available diamonds and analyze their specific traits. It reduces too much human interaction and leads to adding precision. The use of AI reduces the chances of error and adds to the quality of the product. Moreso, the addition of new developments to this field will enhance the mentioned technology further in the future. 

Artificial Intelligence

Snowflake Launches Arctic: The Most Open, Enterprise-Grade Large Language Model

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Snowflake (NYSE: SNOW), the Data Cloud company, today announced Snowflake Arctic, a state-of-the-art large language model (LLM) uniquely designed to be the most open, enterprise-grade LLM on the market. With its unique Mixture-of-Experts (MoE) architecture, Arctic delivers top-tier intelligence with unparalleled efficiency at scale. It is optimized for complex enterprise workloads, topping several industry benchmarks across SQL code generation, instruction following, and more. In addition, Snowflake is releasing Arctic’s weights under an Apache 2.0 license and details of the research leading to how it was trained, setting a new openness standard for enterprise AI technology. The Snowflake Arctic LLM is a part of the Snowflake Arctic model family, a family of models built by Snowflake that also include the best practical text-embedding models for retrieval use cases.

“This is a watershed moment for Snowflake, with our AI research team innovating at the forefront of AI,” said Sridhar Ramaswamy, CEO, Snowflake. “By delivering industry-leading intelligence and efficiency in a truly open way to the AI community, we are furthering the frontiers of what open source AI can do. Our research with Arctic will significantly enhance our capability to deliver reliable, efficient AI to our customers.”

Arctic Breaks Ground With Truly Open, Widely Available Collaboration
According to a recent report by Forrester, approximately 46 percent of global enterprise AI decision-makers noted that they are leveraging existing open source LLMs to adopt generative AI as a part of their organization’s AI strategy.1 With Snowflake as the data foundation to more than 9,400 companies and organizations around the world2, it is empowering all users to leverage their data with industry-leading open LLMs, while offering them flexibility and choice with what models they work with.

Now with the launch of Arctic, Snowflake is delivering a powerful, truly open model with an Apache 2.0 license that permits ungated personal, research, and commercial use. Taking it one step further, Snowflake also provides code templates, alongside flexible inference and training options so users can quickly get started with deploying and customizing Arctic using their preferred frameworks. These will include NVIDIA NIM with NVIDIA TensorRT-LLM, vLLM, and Hugging Face. For immediate use, Arctic is available for serverless inference in Snowflake Cortex, Snowflake’s fully managed service that offers machine learning and AI solutions in the Data Cloud. It will also be available on Amazon Web Services (AWS), alongside other model gardens and catalogs, which will include Hugging Face, Lamini, Microsoft Azure, NVIDIA API catalog, Perplexity, Together AI, and more.

Arctic Provides Top-Tier Intelligence with Leading Resource-Efficiency
Snowflake’s AI research team, which includes a unique composition of industry-leading researchers and system engineers, took less than three months and spent roughly one-eighth of the training cost of similar models when building Arctic. Trained using Amazon Elastic Compute Cloud (Amazon EC2) P5 instances, Snowflake is setting a new baseline for how fast state-of-the-art open, enterprise-grade models can be trained, ultimately enabling users to create cost-efficient custom models at scale.

As a part of this strategic effort, Arctic’s differentiated MoE design improves both training systems and model performance, with a meticulously designed data composition focused on enterprise needs. Arctic also delivers high-quality results, activating 17 out of 480 billion parameters at a time to achieve industry-leading quality with unprecedented token efficiency. In an efficiency breakthrough, Arctic activates roughly 50 percent less parameters than DBRX, and 75 percent less than Llama 3 70B during inference or training. In addition, it outperforms leading open models including DBRX, Mixtral-8x7B, and more in coding (HumanEval+, MBPP+) and SQL generation (Spider), while simultaneously providing leading performance in general language understanding (MMLU).

Snowflake Continues to Accelerate AI Innovation for All Users
Snowflake continues to provide enterprises with the data foundation and cutting-edge AI building blocks they need to create powerful AI and machine learning apps with their enterprise data. When accessed in Snowflake Cortex, Arctic will accelerate customers’ ability to build production-grade AI apps at scale, within the security and governance perimeter of the Data Cloud. 

In addition to the Arctic LLM, the Snowflake Arctic family of models also includes the recently announced Arctic embed, a family of state-of-the-art text embedding models available to the open source community under an Apache 2.0 license. The family of five models are available on Hugging Face for immediate use and will soon be available as part of the Snowflake Cortex embed function (in private preview). These embedding models are optimized to deliver leading retrieval performance at roughly a third of the size of comparable models, giving organizations a powerful and cost-effective solution when combining proprietary datasets with LLMs as part of a Retrieval Augmented Generation or semantic search service.

Snowflake also prioritizes giving customers access to the newest and most powerful LLMs in the Data Cloud, including the recent additions of Reka and Mistral AI’s models. Moreover, Snowflake recently announced an expanded partnership with NVIDIA to continue its AI innovation, bringing together the full-stack NVIDIA accelerated platform with Snowflake’s Data Cloud to deliver a secure and formidable combination of infrastructure and compute capabilities to unlock AI productivity. Snowflake Ventures has also recently invested in Landing AI, Mistral AI, Reka, and more to further Snowflake’s commitment to helping customers create value from their enterprise data with LLMs and AI.

Comments On the News from AI Experts
Snowflake Arctic is poised to drive significant outcomes that extend our strategic partnership, driving AI access, democratization, and innovation for all,” said Yoav Shoham, Co-Founder and Co-CEO, AI21 Labs. “We are excited to see Snowflake help enterprises harness the power of open source models, as we did with our recent release of Jamba — the first production-grade Mamba-based Transformer-SSM model. Snowflake’s continued AI investment is an important factor in our choosing to build on the Data Cloud, and we’re looking forward to continuing to create increased value for our joint customers.”

“Snowflake and AWS are aligned in the belief that generative AI will transform virtually every customer experience we know,” said David Brown, Vice President Compute and Networking, AWS. “With AWS, Snowflake was able to customize its infrastructure to accelerate time-to-market for training Snowflake Arctic. Using Amazon EC2 P5 instances with Snowflake’s efficient training system and model architecture co-design, Snowflake was able to quickly develop and deliver a new, enterprise-grade model to customers. And with plans to make Snowflake Arctic available on AWS, customers will have greater choice to leverage powerful AI technology to accelerate their transformation.”


“As the pace of AI continues to accelerate, Snowflake has cemented itself as an AI innovator with the launch of Snowflake Arctic,” said Shishir Mehrotra, Co-Founder and CEO, Coda. “Our innovation and design principles are in-line with Snowflake’s forward-thinking approach to AI and beyond, and we’re excited to be a partner on this journey of transforming everyday apps and workflows through AI.”

“There has been a massive wave of open-source AI in the past few months,” said Clement Delangue, CEO and Co-Founder, Hugging Face. “We’re excited to see Snowflake contributing significantly with this release not only of the model with an Apache 2.0 license but also with details on how it was trained. It gives the necessary transparency and control for enterprises to build AI and for the field as a whole to break new grounds.”

“Lamini’s vision is to democratize AI, empowering everyone to build their own superintelligence. We believe the future of enterprise AI is to build on the foundations of powerful open models and open collaboration,” said Sharon Zhou, Co-Founder and CEO, Lamini. “Snowflake Arctic is important to supporting that AI future. We are excited to tune and customize Arctic for highly accurate LLMs, optimizing for control, safety, and resilience to a dynamic AI ecosystem.”

“Community contributions are key in unlocking AI innovation and creating value for everyone,” said Andrew Ng, CEO, Landing AI. “Snowflake’s open source release of Arctic is an exciting step for making cutting-edge models available to everyone to fine-tune, evaluate and innovate on.”

“We’re pleased to increase enterprise customer choice in the rapidly evolving AI landscape by bringing the robust capabilities of Snowflake’s new LLM model Arctic to the Microsoft Azure AI model catalog,” said Eric Boyd, Corporate Vice President, Azure AI Platform, Microsoft. “Our collaboration with Snowflake is an example of our commitment to driving open innovation and expanding the boundaries of what AI can accomplish.”

“The continued advancement — and healthy competition between — open source AI models is pivotal not only to the success of Perplexity, but the future of democratizing generative AI for all,” said Aravind Srinivas, Co-Founder and CEO, Perplexity.We look forward to experimenting with Snowflake Arctic to customize it for our product, ultimately generating even greater value for our end users.”
“Snowflake and Reka are committed to getting AI into the hands of every user, regardless of their technical expertise, to drive business outcomes faster,” said Dani Yogatama, Co-Founder and CEO, Reka. “With the launch of Snowflake Arctic, Snowflake is furthering this vision by putting world-class truly-open large language models at users’ fingertips.”

“As an organization at the forefront of open source AI research, models, and datasets, we’re thrilled to witness the launch of Snowflake Arctic,” said Vipul Ved Prakash, Co-Founder and CEO, Together AI. “Advancements across the open source AI landscape benefit the entire ecosystem, and empower developers and researchers across the globe to deploy impactful generative AI models.”

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Vechain and SingularityNet Combine Blockchain + AI To Drive Sustainability and Build Advanced Enterprise-Grade Tools

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Vechain and SingularityNET, industry leaders in blockchain and artificial intelligence (AI) respectively, have announced their strategic collaboration. This partnering of technical giants unites powerful emerging technologies with the potential to radically change how the global economy operates, offering powerful enterprise-grade tools to tackle challenges in the field of sustainability and traditional businesses.

In particular, the alliance holds great promise for vechain’s ambitions with Boston Consulting Group, partners, collaborating on building ‘ecosystems’ wherein individuals and businesses are incentivised to act sustainably. SingularityNet’s AI capabilities offer immense potential to enhance and improve these ecosystems, utilising AI technology to pore over data, and improve their efficacy.

Vechain and SingularityNET intend to launch joint research initiatives to fortify the efficacy of each respective platform and ingrain the pair at the heart of future digital development. The combination of these technologies can equip businesses with intelligent tools, signalling the onset of a new phase in the era of digitisation.

Dr. Ben Goertzel, the visionary CEO of SingularityNET, expressed his excitement for the massive potential of this partnership:

“The last few years have taught the world that when the right AI algorithms meet the right data on sufficient processing power, magic can happen.

What’s even better is when the algorithms, data and processing are decentralized in deployment, ownership and control — which is exactly the sort of magic that’s going to happen putting the SingularityNET ecosystem’s AI algorithms together with vechain’s deep and diverse enterprise data, on the joint, secure distributed processing power of the two networks.

This combined power will be applicable to sustainability as one of our initial focus areas, but in the end extends across essentially all vertical markets. It’s hard to overestimate the potential here.”

Vechain’s CTO Antonio Senatore commented:

“We’re excited to be collaborating with leading Web3 AI platform, SingularityNET, combining our rich streams of enterprise data with SingularityNET’s powerful and versatile platform.”

“Blockchain and AI offer game-changing capabilities for industries and enterprises and are opening new avenues of operation. We look forward to working closely with the SingularityNET team to build out new services and continue to advance the fore of possibility in web3 and sustainability.”

Vechain and SingularityNET are enabling a new, more interconnected and automated world, driving new capabilities in the fields of industry and in particular, for action around sustainability.

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Deepdub and OOONA Announce Strategic Partnership to Expand AI-Based Dubbing Solutions to Global Entertainment and Media Clients

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Deepdub, the leading AI-based audiovisual dubbing and language localization company, today announced a partnership with OOONA, a major media localization software provider. This collaboration will bring Deepdub’s advanced dubbing solutions to OOONA’s extensive entertainment and media clients worldwide.

Through this partnership, OOONA will implement a process for connecting their clients to Deepdub’s services. This will enable media companies and content creators worldwide to instantly access Deepdub’s innovative dubbing solutions. Companies will be able to submit their content localization needs with ease via OOONA’s platform and receive tailored proposals from Deepdub that leverage the power of AI emotion-prompting technology. Going forward, clients stand to benefit from more efficient workflows and access to groundbreaking dubbing capabilities unlocking flexibility and scale.

“OOONA’s unmatched expertise in media localization, honed from providing pioneering management and production tools to the biggest names in the sector, makes them an ideal partner,” said Ofir Krakowski, CEO and co-founder of Deepdub. “This collaboration gives us the opportunity to introduce our advanced AI dubbing technology to new clients across the entertainment industry and beyond.”

OOONA is trusted by leading media localizers, broadcasters and a vast user base spanning over 170 countries. “We continue to stay true to our mission of being the core platform that integrates anything our clients need, including any opportunities AI-based solutions bring for localizing audiovisual assets,” said Wayne Garb, CEO and co-founder of OOONA. “We are thrilled to collaborate with Deepdub and further strengthen the services we provide to our customers globally.”

About Deepdub

Deepdub aims to bridge the language barrier and cultural gap of entertainment experiences for international audiences across TV, Film, Advertising, Gaming and e-learning. We provide a high-quality localization service for entertainment content using deep learning and AI algorithms. Deepdub plugs into the post-production process of content owners and provides an end-to-end solution for all of their localization needs. Deepdub’s team consists of technology entrepreneurs, engineers, and scientists, as well as dubbing and post-production specialists with extensive industry experience. The advisory board features prominent media executives such as Kevin Reilly, who held the position of Chief Content Officer at HBO Max and president of TNT, TBS, and truTV, and Emiliano Calemzuk, the former President of Fox Television Studios.

For more information about Deepdub, visit https://deepdub.ai 

About OOONA

OOONA.Net Ltd (www.ooona.net) is a globally recognized provider of professional management and production tools for the media localization industry. Renowned for its state-of-the-art software catering to subtitling, voiceover, dubbing and captioning needs, OOONA’s modular, pay-as-you-go pricing model empowers users to tailor solutions to their unique requirements. Trusted by leading media localizers, broadcasters and a vast user base spanning over 170 countries, OOONA continues to trailblaze advancements in the field of media localization.

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