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Many organizations are under strict data security and governance guidelines, or have multi-cloud constraints, and thus can’t solely leverage cloud-hosted generative AI models. But that doesn’t stop these organizations from staying at the forefront of the AI and LLM revolution. DataRobot, when paired with Nutanix’s GPT-in-a-Box solution, delivers cutting-edge training and inference for predictive and generative models that can be deployed in hours while maintaining governance, compliance, observability, and most importantly, security. This entire turnkey solution can be deployed in an air-gapped environment anywhere in the world, even in the most secure or extreme locations. 

Announced at Nutanix .NEXT 2024 in Barcelona, this partnership combines streamlined data center operations and GPT-in-a-Box from Nutanix with the leading AI platform for governance from DataRobot. Together, Nutanix and DataRobot give enterprises the only full-stack offering in the market for achieving AI sovereignty, bringing the latest capabilities into a secure on-prem environment and giving organizations complete control over their data and their AI stack.

This means that companies with the highest security standards have a clear path past the biggest hurdles encountered when building an on-prem stack, resulting in faster time-to-market and higher ROI while delivering the  flexibility to adapt and keep up with innovation. 

Nutanix: Empowering Intelligent, Scalable Enterprises

Enterprise AI with Nutanix GPT–in-a-Box is a key solution to help customers deploy, manage, and adapt predictive and generative AI, allowing for a tailored AI strategy. Built on Nutanix Cloud Platform, this advanced edge-to-cloud infrastructure solution enables customers to run inference seamlessly and further integrate AI apps into their business processes using ready-to-use, pre-trained AI models.

Enterprises can also ensure their data is trustworthy,  resilient, and built on stringent ethics policies and data privacy standards. This unleashes  the responsible use of AI with the full transparency and compliance that empower  businesses to innovate confidently and sustainably.

Nutanix customers that leverage GPT-in-a-Box with the DataRobot AI Platform are realizing how simple it is to achieve their AI productivity goals. The scale and security of our combined solution is unmatched.

DataRobot: The Unified AI Platform for On-Prem, Cloud, and Hybrid Cloud

The DataRobot AI Platform is an open, complete AI lifecycle platform for predictive and generative AI that has broad interoperability and end-to-end capabilities to help organizations build, operate, and govern their entire AI stack. Built for enterprise-grade use, the DataRobot AI Platform can be deployed on-premises or in any cloud infrastructure. 

DataRobot streamlines and accelerates the process of building impactful AI apps and simplifies the process of monitoring those apps, no matter where they’re deployed. This enables  organizations to move past infrastructure problems and focus on solving business problems. DataRobot’s robust governance tools makes it easy to manage users, ensure models are safe before they’re deployed, and ensure complete regulatory compliance. 

We’re dedicated to helping our customers build, use, and manage AI safely. We’re excited to work with Nutanix to create the GPT-in-a-box solution, which will help organizations speed up their journey to AI value with enterprise-grade security, performance, and control. 

DataRobot & Nutanix: End-to-End Platform Experience

A growing number of organizations are adopting  on-premises data center or private cloud deployments as part of  their AI stack. 59% plan to run AI inference workloads on-prem or in private cloud demanding simplified, unified tooling:

• Streamlined Integration: AI stacks can take weeks — or even months — to properly ramp up, extending the time to ROI and distracting data science teams from more valuable work. Luckily, Nutanix and DataRobot have done the hard work to ensure that security-minded teams can set up their AI stack in days, significantly shortening the path to AI impact.   (Link to Nutanix + DataRobot TechNote)
• Total Data Privacy: Many organizations are turning to open source AI models and tools to ensure transparency for their enterprise AI efforts. Nutanix and DataRobot provide these models alongside enhanced data security, governance, and best practices for a transparent and highly secure approach to AI that meets the most stringent standards.
• End-to-End Experience: Eliminate tool sprawl, complicated licensing, and reduce silos with a complete turnkey enterprise AI solution. 

See DataRobot and Nutanix in Action

This demo share how Nutanix and DataRobot can help you:

✅ Deploy LLMs like NVIDIA NIM or Hugging Face

✅ Set up a GPU-powered endpoint

✅ Register that endpoint within DataRobot

✅ Evaluate and compare that LLM within DataRobot LLM Playground

✅ Deploy that model with guard models like NeMo Guardrails

✅ Set up comprehensive monitoring with DataRobot Console

Watch the demo now:

Bring AI Innovation to your On-Premises Environment

Schedule a demo with our expert data scientists to see how you can quickly stand up your AI stack and build AI apps that solve your most critical use cases in weeks, not months.

The post DataRobot and Nutanix Partner to Deliver Turnkey AI for On-Premises Deployments appeared first on DataRobot.

The demand for AI robots is driven by the need for automation and efficient data management across various industries. In manufacturing, AI robots are used to automate complex assembly lines, reducing labor costs and increasing productivity.

In the ever-evolving field of robotics, a groundbreaking approach has emerged, revolutionizing how robots perceive, navigate, and interact with their environments. This new frontier, known as brain-inspired navigation technology, integrates insights from neuroscience into robotics, offering enhanced capabilities and efficiency.

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

Non-living hydrogels can play the video game Pong and improve their gameplay with more experience, researchers report. The researchers hooked hydrogels up to a virtual game environment and then applied a feedback loop between the hydrogel's paddle -- encoded by the distribution of charged particles within the hydrogel -- and the ball's position -- encoded by electrical stimulation. With practice, the hydrogel's accuracy improved by up to 10%, resulting in longer rallies. The researchers say that this demonstrates the ability of non-living materials to use 'memory' to update their understanding of the environment, though more research is needed before it could be said that hydrogels can 'learn.'

In an innovative leap forward for workplace safety, a research team at Seoul National University has developed the Bilateral Back Extensor Exosuit (BBEX), a robotic back-support device designed to prevent spinal injuries and assist workers in heavy lifting tasks.

Robotic automation has become a game-changer in addressing labor shortages. While traditional rigid grippers have effectively automated various routine tasks, boosting efficiency and productivity in industries that deal with objects of well-defined specifications, they fall short in sectors like the food industry, where delicate objects of varying sizes and shapes need to be handled. In these cases, a more specialized type of gripper is required.

With omnidirectional movement and smart docking with virtually any warehouse cart in under 20 seconds, these new robots are making material handling faster and safer.

West Virginia University roboticists are working on an alternative path to robot autonomy in Loopy, a "multicellular robot" composed of a ring of individual interconnected robot cells.

A multidisciplinary research team based across China and Brazil has used a dog-like robot and AI to create a new way to find fire ant nests. Published in the journal Pest Management Science, the study highlights how a "CyberDog" robot integrated with an AI model can automate the identification and control of Red Imported Fire Ants (RIFA), a globally destructive pest.

An insect species that evolved 130 million years ago is the inspiration for a new research study to improve navigation systems in drones, robots, and orbiting satellites.

By processing vast amounts of data in real time, AI-driven robots can make intelligent decisions, optimize operations, and continuously improve over time. This advancement is redefining the way warehouses operate, offering enhanced productivity and reliability.

Transforming Credit Card Management: The Impact of AI and ML

The role of AI and ML in transforming credit risk management in banking

Credit-card fraud has been a prime challenge for customers and financial organizations in this digital age. Globally, more than $28 billion was lost last year from credit card fraud. It is going to rise in the future, and hence there is a need for robust risk management mechanisms.

Previously, risk in credit card portfolios was managed through a variety of manually developed procedures for fraud detection and prevention. However, traditional methods have become ineffective against today’s intelligent hacking methods.

Fortunately, the advent of AI and ML has transformed credit card risk management processes. These technologies process huge volumes of data and can effectively detect anomalies, mitigating threats. This technological shift shall create a better transaction security experience for customers through reduced false positives and smoother and safer transactions.

The article will discuss how AI and ML can solve traditional credit card risk management problems. We are also going to look into the different techniques used, the benefits of using AI and ML for credit card risk management, and some case studies with real-world examples.

An Overview of Credit Card Risk Management

It is the process of identifying, assessing, and mitigating the risks associated with credit card transactions. Therefore, this whole process can be regarded as paramount to protecting consumers and even financial institutions against fraudulent activities.

Traditionally, credit card risk management depended on rule-based systems and manual reviews. In the rule-based system, as the name implies, predefined criteria are used in the identification of risky transactions. For example, transactions exceeding a certain amount or originating from unusual locations raise red flags. While providing some form of security, these measures were typically inadequate to handle increasing volumes and sophistication in credit-card transactions.

This can often result in the generation of a lot of false positives by rule-based systems misinterpreting a legal transaction as fraud. This might anger customers and put additional workload on customer service. Further, fraudsters are continuously inventing new techniques that make it pretty hard for the static rule-based system to detect new threats.

The Role of AI and ML in Credit Card Risk Management

1. Transforming Risk Management

AI and ML have transformed credit card risk management with much more accurate, efficient, and dynamic approaches toward fraud detection and mitigation. These technologies use large datasets and complex algorithms that enable identifying trends and outliers in real time. This approach opens up avenues for proactive threat detection and response.

2. Real-Time Fraud Detection

AI and ML systems are very good at real-time fraud detection, for they continuously monitor transactions and user behavior. While traditional, rule-based approaches could not detect these newer styles of distributed fraud schemes, AI and ML adapt rapidly to new patterns of fraud once they appear. This will ensure that financial institutions are always one step ahead of fraudsters in the identification of suspicious activities before they can cause great damage.

3. Pattern Recognition and Anomaly Detection

The major strengths of AI and ML in risk management are their ability to identify complex patterns and detect anomalies indicative of fraudulent behavior. Such systems create baseline behaviors through the analysis of historical transaction data, user profiles, and contextual information. Deviations from these norms trigger alerts for further investigation. This level of precision helps in distinguishing genuine transactions from fraudulent ones, thus reducing the incidence of false positives.

4. Continuous Learning and Improvement

These AI and ML models learn from new data continuously, which can only improve their fraud detection capabilities over time. As more transactions are processed and different fraud scenarios unfold, these models will fine-tune their algorithms to be more accurate and efficient. This is an ongoing spiral of improvement, ensuring the risk management system will change as the landscape of fraud evolves.

5. Automation and Efficiency

AI and ML can substantially reduce the need for reviews subject to risk management by automating a number of aspects involved in this particular area. Automated systems can process volumes of data at lengths and speeds inconceivable to any human analyst, allowing for effective fraud detection in a timely manner. This not only improves operational efficiency but also frees human resources to deal with more complex and risky cases that require nuanced decision-making.

6. Integration with Existing Systems

AI and ML technologies can be combined with pre-existing frameworks of risk management, thereby delivering greater potential and effectiveness without requiring an overhaul in their entirety. Equally, this will give a financial institution a chance to utilize the current running infrastructure and receive all the benefits coming from advanced AI and ML-driven insights. The result is going to be an ultimately more solid, responsive system of risk management that is able to adapt itself to polished threats and challenges.

Key Challenges and Considerations To Overcome

While AI and ML have huge potential in credit card transaction risk management, there are also a number of pitfalls and issues that arise with their implementation. Bringing solutions to these problems will be key to maximizing the effectiveness of the technologies and their ethical working.

1. Data Privacy

Since AI and ML systems are data-driven, the door is also wide open to a number of potential data privacy and security issues. However, the financial institutions should be in a position to protect sensitive customer information and establish mechanisms of data collection, storage, and processing that consider applicable privacy regulations under GDPR and CCPA; this argues further that appropriate encryption methods, access controls for customers, and good anonymization techniques will be implemented accordingly.

2. Regulatory Compliance

One of the major challenges institutions face in implementing AI and ML is managing the complex landscape of financial regulations relevant to the use and processing of data. This requires stringent controls and the associated transparency expected by regulators. Hence, institutions will have to ensure compliance with these regulations, which may involve regular audits, documentation, and reporting of AI and ML models to regulatory authorities.

3. Implementation Barriers

AI and ML technologies can be resource-intensive to adopt. Some of the common challenges faced while implementing AI and ML include:

• High Costs: Setting up AI and ML infrastructures, software, and personnel with exceptionally high skills can be really resource-intensive.
• Technical Complexity: The development and maintenance of AI and ML systems require special knowledge and expertise that are often absent in some organizations.
• Integration problems: The overall incorporation of AI and ML into underpinning systems and workflows has proved to be quite problematic. Careful thought in planning and execution will be needed if the technology is to work seamlessly.

Real-time Case Studies and Real-World Examples

Real-world examples will attest to how AI and ML find practical applications in credit card risk management. Case studies will illustrate how financial institutions have tapped into such technologies to frustrate fraud, enhance security, and improve customer satisfaction.

Many of the leading financial institutions have included AI and ML within the risk management framework with quite excellent results:

• JPMorgan Chase: AI-driven systems for tracking and analyzing millions of transactions every day have been put in place. Its AI models detect fraudulent activities with high accuracy, reducing false positives drastically and increasing the security of overall transactions.
• HSBC: ML algorithms at HSBC enhance their capability for fraud detection. Analysis of the historical transaction data will help in discovering the spend pattern; therefore, these companies can ensure prevention and prediction accordingly. This proactive policy has caused a notable decrease in fraud-related losses.

USM Business systems is also a pioneer in AI-powered mobile app development fraud detection. We help you keep your and yours esteemed customers data privacy and financial safety through developing high-quality advanced credit risks management apps.

Conclusion

AI/ML-powered mobile app development for fraud detection is the best option in this digital age. These technologies provide better accuracy, real-time processing, cost efficiency, and an improved customer experience.

At the same time, all the challenges and considerations involved do not overshadow the bright future awaiting AI and ML in risk management. Only those financial institutions that embrace these technologies will adapt to the ever-changing landscape of credit card fraud and ensure safe and satisfied customers.

 

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Researchers have used artificial intelligence tools to accelerate the understanding of the risk of specific cardiac arrhythmias when various parts of the heart are exposed to different thresholds of radiation as part of a treatment plan for lung cancer.