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AI concerns continue as governments look for the right mix of regulations and protections FCW

AI governance: Key for addressing the Executive Order on safe, secure and trustworthy artificial intelligence

Secure and Compliant AI for Governments

This paper highlights the ways in which state and local government can take advantage of generative AI while using it responsibly. In digitizing their services, government departments now hold highly confidential people, business and national data, and given the sensitivity of this data, they constantly face attacks from sophisticated, malicious actors. What’s more, it’s imperative their services are operational given their criticality and impact, hence the need for resilient and strong cyber practices. Azure Government customers, including US federal, state, and local government agencies and their partners, can now leverage the Microsoft Azure OpenAI Service. Purpose-built, AI-optimized infrastructure provides access to advanced generative models developed by OpenAI, such as GPT-4, GPT-3, and Embeddings.

Why is artificial intelligence important in government?

By harnessing the power of AI, government agencies can gain valuable insights from vast amounts of data, helping them make informed and evidence-based decisions. AI-driven data analysis allows government officials to analyze complex data sets quickly and efficiently.

For example, Gartner predicts that by 2026, 60% of government organizations will prioritize business process automation through hyperautomation initiatives to support business and IT processes in government to deliver connected and seamless citizen services. This article will highlight how AI-powered tools, like copilots, can streamline operations, boost productivity, and transform how citizens access services. We’ll cover everything from critical use cases to challenges to workforce implications. In a blog post shared exclusively with FedScoop that will publish Tuesday, Microsoft noted the of security and compliance required by government agencies when handling sensitive data. “To enable these agencies to fully realize the potential of AI, over the coming months Microsoft will begin rolling out new AI capabilities and infrastructure solutions across both our Azure commercial and Azure Government environments,” the blog post stated. Microsoft Azure Government maintains strict compliance standards to protect data, privacy, and security and provides an approval process to modify content filters and data logging.

Cybersecurity resolutions: how to make 2024 safer

Artificial intelligence, like Frankenstein’s monster, may appear human, but is decidedly not. Despite the popular warnings of sentient robots and superhuman artificial intelligence that grow more difficult to avoid with each passing day, artificial intelligence as it is today possesses no knowledge, no thought, and no intelligence. In the future, technical advancements may one day help us to better understand how machines can learn, and even learn how to embed these important qualities in technology.

Why is AI governance needed?

AI governance is needed in this digital technologies era for several reasons: Ethical concerns: AI technologies have the potential to impact individuals and society in significant ways, such as privacy violations, discrimination, and safety risks.

In the private sector, regulators should make compliance mandatory for high-risk uses of AI where attacks would have severe societal consequences, and optional for lower-risk uses in order to avoid disrupting innovation. With the EU AI Act expected to come into force in 2024, it’s clear that comprehensive AI legislation is on the horizon. In this article, we’ll highlight how many companies, including deepset, are already working hard to ensure that their generative technology offerings comply with existing and emerging regulations – and to provide users with the highest standard of security for their generative AI technology. Furthermore, OMB has been further tasked with establishing systems to ensure agency compliance with guidance on AI technologies, including ensuring agency contracts for purchasing AI systems align with all legal and regulatory requirements and yearly cataloging of agency AI use cases. With careful adoption, conversational AI enables public sector agencies to deliver better services to citizens through automation and data-driven insights. The technology opens the door for more efficient, inclusive, and responsive governance.

Make your government content inclusive, multilingual and secure with AI-Media

EPIC’s work is funded by the support of individuals like you, who help us to continue to protect privacy, open government, and democratic values in the information age. Leverage a leading enterprise Agile planning solution to scale Agile best practices and gain the flexibility to modernize application delivery without the need to replace existing technology. CMMC 2.0 is expected to become the official standard for cybersecurity certification in… Manually copying data from various spreadsheets and word documents when running an audit or assessment to produce static reports is extremely time-consuming, error prone and inefficient. The tech giant’s Teams Premium service with intelligent recap of meetings is expected to roll out to government users during the spring of 2024. Intelligent recap uses AI to help users summarize meeting content and focus on key elements through AI-generated meeting notes and tasks.

(ii)  as part of the AI Tech Sprint competitions and in collaboration with appropriate partners, provide participants access to technical assistance, mentorship opportunities, individualized expert feedback on products under development, potential contract opportunities, and other programming and resources. Independent regulatory agencies are encouraged, as they deem appropriate, to consider whether to mandate guidance through regulatory action in their areas of authority and responsibility. Accelerate content creation, communication and understanding with our GDPR-compliant AI content platform that writes in your tone of voice.Our AI content tool ensures that all data is handled and protected in compliance with GDPR regulations. AI can be a fundamental source of competitive advantage, helping organizations meet challenges and uncover opportunities for now and in the future.

However, just as not all applications of AI are “good,” not all AI attacks are necessarily “bad.” As autocratic regimes turn to AI as a tool to monitor and control their populations, AI “attacks” may be used as a protective measure against government oppression, much like technologies such as Tor and VPNs are. For more information on federal programs and policy on artificial intelligence, visit Public sector organizations embracing conversational AI stand to be further ahead of their counterparts due to the technology’s ability to optimize operational costs and provide seamless services to its citizens. NB Defense is an open source offering for Jupyter notebooks that quickly scans notebook(s) for common security issues, identifies potential risks, and guides your remediation. Available now, this tool helps your teams quickly get started with protecting your AI from risks. At Securiti, our mission is to enable enterprises to safely harness the incredible power of data and the cloud by controlling the complex security, privacy and compliance risks.

Secure and Compliant AI for Governments

5 Production machine learning systems may feature a good amount of human and guard rail engineering, while others may be fully data dependent. As a result, some production systems may fall along a spectrum between “learned” systems that are fully data dependent and “designed” systems that are heavily based on hand-designed features. However, systems that are closer to the “designed” side of the spectrum may still be vulnerable to attacks, such as input attacks.

In the context of military operations in armed conflict, the United States believes that international humanitarian law (IHL) provides a robust and appropriate framework for the regulation of all weapons, including those using autonomous functions provided by technologies such as AI. Building a better common understanding of the potential risks and benefits that are presented by weapons with autonomous functions, in particular their potential to strengthen compliance with IHL and mitigate risk of harm to civilians, should be the focus of international discussion. The United States supports the progress in this area made by the Convention on Certain Conventional Weapons, Group of Governmental Experts on Emerging Technologies in the Area of Lethal Autonomous Weapon Systems (GGE on LAWS), which adopted by consensus 11 Guiding Principles on responsible development and use of LAWS in 2019. The State Department will continue to work with our colleagues at the Department of Defense to engage the international community within the LAWS GGE. Offering multiple foundation models (FMs) is especially important to the public sector, which comprises governments, educational institutions, nonprofits, aerospace entities, and health care organizations that are exploring how to use generative AI to satisfy the evolving needs of citizens around the globe. We’re excited that AWS Partners are using these technologies to address challenges like securely managing complex data sets, detecting cybersecurity threats, and more.

Secure and Compliant AI for Governments

(ff)  The term “testbed” means a facility or mechanism equipped for conducting rigorous, transparent, and replicable testing of tools and technologies, including AI and PETs, to help evaluate the functionality, usability, and performance of those tools or technologies. (ee)  The term “synthetic content” means information, such as images, videos, audio clips, and text, that has been significantly modified or generated by algorithms, including by AI. (h)  The term “critical and emerging technologies” means those technologies listed in the February 2022 Critical and Emerging Technologies List Update issued by the National Science and Technology Council (NSTC), as amended by subsequent updates to the list issued by the NSTC. Typetone AI allows you to streamline your workflow by integrating with any existing system you use, such as CMS, ATS, CRM, and more.

By switching valid data with poisoned data, the machine learning model underpinning the AI system itself becomes poisoned during the learning process. As a toy example of this type of poisoning attack, consider training a facial recognition-based security system that should admit Alice but reject Bob. If an attacker poisons the dataset by changing some of the images of “Alice” to ones of “Bob,” the system would fail in its mission because it would learn to identify Bob as Alice. Therefore Bob would be incorrectly authenticated as Alice when the system was deployed.

Powered by AI, LEXI’s unmatched accuracy and cutting-edge features deliver results that rival human captions, at a fraction of the cost. It seamlessly integrates with LEXI Viewer, the ultimate HD-SDI captioning device for your event presentations. This combination ensures captions are clear and easy to read, while keeping video content fully visible. Then, once you’ve worked on and tested your prompts to get them working the way you want, you can start automating mundane tasks such as translating documents into JSON files. From there, you can put that in a pipeline, run it at scale across a large set of documents, and apply it to a line of your business applications.

Learn how the AWS Intelligence Initiative is providing new career paths for engineers

In the future, red teaming will be essential for any high-risk AI system, not just the foundational models. In the near future, AI systems will have specific requirements depending on the domain in which they’re deployed. The EO introduces a wide range of critical guidelines aimed at the privacy, security, and safety of AI technologies. With care, transparency, and responsible leadership, conversational AI can unlock a brighter future, one where high-quality public services are profoundly more accessible, inclusive, and personalized for all. With planning, government workforces can be augmented and empowered by conversational AI rather than displaced. Change management and inclusive policies that support workers will enable the public sector to tap the full potential of AI while ensuring no one is left behind.

  • AI Governance also facilitates compliance with industry-specific regulations, such as HIPAA for healthcare or FINRA for financial services.
  • In many contexts, these assets are currently not treated as secure assets, but rather as “soft” assets lacking in protection.
  • If confronted with better content filters, they are likely to be the first adopters of AI attacks against these filters.
  • The past decade has borne poisonous fruit from technological seeds planted before the turn of the century.

Algorithms provide the rules and context for AI as it begins to sort and analyze data, providing structure as AI learns. Domino also gives code-first data scientists and researchers the flexibility and freedom to support what they do best — solve problems without technical hurdles. Domino’s model factory eliminates backlogs by automating model governance, validation, production, monitoring and performance tracking. And Domino centralizes AI workflows, so agencies always know which models are being consumed so they can stay aligned with sponsors and measure impact. Making AI a trustworthy tool for decision-making requires traceability to ensure accountability, observability to remove model biases and limitations, and enterprise-grade governance so models are responsibly built from day one. Individuals should take appropriate steps to ensure the security of their devices and accounts.

Secure and Compliant AI for Governments

The military will need to develop protocols that prioritize early identification of when its AI algorithms have been hacked or attacked so that these compromised systems can be replaced or re-trained immediately. Hardening these “soft” targets will be an integral component of defending against AI attacks. This is because the two prominent forms of AI attacks discussed here, input and poisoning attacks, are easier to execute if the attacker has access to some component of the AI system and training pipeline. This has transformed a wide range of assets that span the AI training and implementation pipelines into targets for would-be attackers. Specifically, these assets include the datasets used to train the models, the algorithms themselves, system and model details such as which tools are used and the structure of the models, storage and compute resources holding these assets, and the deployed AI systems themselves.

Is AI a security risk?

AI tools pose data breach and privacy risks.

AI tools gather, store and process significant amounts of data. Without proper cybersecurity measures like antivirus software and secure file-sharing, vulnerable systems could be exposed to malicious actors who may be able to access sensitive data and cause serious damage.

Unlike humans, machine do not tire.”48 Beyond just its use in keeping pace with expanding amounts of content, AI can be used to provide more effective policing and crime prevention by detecting criminal warning signs earlier and apprehending suspects faster. Beyond the threats posed by sharing datasets, the military may also seek to re-use and share models and the tools used to create them. Because the military is a, if not the, prime target for cyber theft, the models and tools themselves will also become targets for adversaries to steal through hacking or counterintelligence operations. History has shown that computer systems are an eternally vulnerable channel that can be reliably counted on as an attack avenue by adversaries.

Secure and Compliant AI for Governments

If the adversary controls the entities on which data is being collected, they can manipulate them to influence the data collected. Because the adversary has control over their own aircraft, it can alter them in order to alter the data collected. Adversaries need not be aware that data is being collected in order to manipulate the process. The existence of the possibility that data will be collected may be enough of a threat to execute this type of influence campaign. Beyond this supportive role, regulators should affirm that they will use an entity’s effort in executing a suitability test in deciding culpability and responsibility if attacks do occur.

Read more about Secure and Compliant AI for Governments here.

How AI can be used in government?

The federal government is leveraging AI to better serve the public across a wide array of use cases, including in healthcare, transportation, the environment, and benefits delivery. The federal government is also establishing strong guardrails to ensure its use of AI keeps people safe and doesn't violate their rights.

What is AI in governance?

AI governance is the ability to direct, manage and monitor the AI activities of an organization. This practice includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.

How can AI improve the economy?

AI has redefined aspects of economics and finance, enabling complete information, reduced margins of error and better market outcome predictions. In economics, price is often set based on aggregate demand and supply. However, AI systems can enable specific individual prices based on different price elasticities.

AI is the Future of Manufacturing, and It’s Already Here

Top 13 Use Cases Applications of AI in Manufacturing in 2023

ai in factories

If you aren’t already considering how AI could impact your line of work, you should start thinking about it now. Learn how to identify anomalies and failures in time-series data by using AI to estimate the condition of equipment and predict when maintenance should be performed. With NVIDIA Omniverse™, the automaker is bringing the power of industrial AI to its entire production network as part of its digital transformation.

A factory filled with robot workers once seemed like a scene from a science-fiction movie, but today, it’s just one real-life scenario that reflects manufacturers’ use of artificial intelligence. A lights-out factory is a smart factory that’s capable of operating entirely autonomously without any humans on site. Some examples of this in practice include Pepsi and Colgate, which both use technology designed by AI startup Augury to detect problems with manufacturing machinery before they cause breakdowns. Generative design is a bit like the generative AI we’ve seen in technologies like ChatGPT or Dall-E, except instead of telling it to create text or images, we tell it to design products. Cobots are widely used by automotive manufacturers, including BMW and Ford, where they perform tasks including gluing and welding, greasing camshafts, injecting oil into engines, and performing quality control inspections. Computer vision, which employs high-resolution cameras to observe every step of production, is used by AI-driven flaw identification.

Steel Manufacturer Reduces Scrap Rates – and Costs – with AI

In this scenario, it would be much slower for a human to examine each product and determine if code labels are correctly attached, readable and then determine the next course of action. Machines in combination with AI can do this work many times faster and with fewer errors. Therefore, it is necessary that the System would provide correct and reasonable results. Therefore Explainable AI is required to know the mistakes that the System can make and the safety measures.

AI can replace human labor, optimize inventory, and ensure equipment stability, reducing expenses and improving cost management. AI can be also used to optimize manufacturing processes and to make those processes more flexible and reconfigurable. Current demand can determine factory floor layout and generate a process, which can also be done for future demand. That analysis then determines whether is it better to have fewer large additive machines or lots of smaller machines, which might cost less and be diverted to other projects when demand slows. A. AI is helping the manufacturing industry by improving efficiency, reducing costs, enhancing product quality, optimizing inventory management, and predicting maintenance needs. The technology is also assisting enterprises with data-driven decision-making, and driving innovation and productivity across the entire manufacturing lifecycle.

Take a Deeper Dive Into AI in Manufacturing

By imbuing this system with artificial intelligence and self-learning capabilities manufacturers can hours by drastically reducing false-positives and the hours required for quality control. Thanks to IoT sensors, manufacturers can collect large volumes of data and switch to real-time analytics. This allows manufacturers to reach insights sooner so that they can make operational, real-time data-driven decisions. An alternative to a custom-built AI solution is a data-centric vertical AI platform, which can facilitate specific use cases. For example, an automated anomaly detection tool could replace or augment human workers who are tasked with quality control.

ai in factories

For instance, BMW employs AI-driven automated guided vehicles (AGVs) in their manufacturing warehouses to streamline intralogistics operations. These AGVs follow predetermined paths, automating the transportation of supplies and finished products, thereby enhancing inventory management and visibility for the company. Even though AI presupposes the surge in robotic automation systems, machine learning technologies are constantly evolving. If there are enough skillful data scientists on your in-house team, good for you.

This journey is marked by the evolution from basic automation to sophisticated AI-driven decision-making and problem-solving. It’s essential to align AI adoption with business objectives and scale incrementally, considering the organization’s readiness and technological capabilities. AI algorithms learn from data, and if that data is biased, the AI’s decisions can perpetuate those biases.

When deploying OpenAI, you’ll need to consider things like security, scalability, performance, data quality and ethics. Contact us to discuss the possibilities and see how we can help you take the next steps towards the future. Siemens outfits its gas turbines with hundreds of sensors that feed into an AI-operated data processing system, which adjusts fuel valves in order to keep emissions as low as possible. We’ve gathered 10 examples of AI at work in smart factories to bridge the gap between research and implementation, and to give you an idea of some of the ways you might use it in your own manufacturing.

These machines are extremely specialized and are not in the business of making decisions. They can operate supervised by human technicians or they can be unsupervised. Since they make fewer mistakes than humans, the overall efficiency of a factory improves greatly when augmented by robotics. The company and Google are using AI algorithms, cloud-based analytics, and computer vision to improve shop floor productivity.

ai in factories

To handle this time-consuming and exhausting task, an AI-based bot was introduced to free up operators for more valuable and complex manufacturing-undertakings. A robot developed in just two and a half days successfully completed this task, opening and printing documentation as it was required, freeing up the operators. Their soda factories needed help with reading labels with manufacturing and expiration dates. Sometimes the tags got smudged because they were put on before the surface was dry.

How Industrial AI is Revolutionizing Manufacturing Operations – Top AI Use Cases in Manufacturing

In such a system, for instance, an AI algorithm can determine how many supplies are entering into the warehouse and going out for the supplies. It helps you monitor the movement of supplies and materials, they can detect empty shelves quickly, alerting managers when stocks need to be replenished. Getting a comprehensive view of the inventory in a warehouse can be challenging, and there will always be some degree of inefficiency. But if you want to minimize those inefficiencies, be as accurate as you can.

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How To Buy An Ai Solution The Right Way: 7 Questions New Customers Should Consider Beritaja

How artificial intelligence is transforming the world

How to Buy an AI Solution for Business The Right Way: 9 Questions New Customers Should Consider

Feeling analytics can identify customer insights with scale and cost-efficiently. Given that emotional data are personal and in context, understanding customers in context provides richer insights about who they are and what they like. Current market analysis, although marching toward machine learning-based analysis rapidly, still relies heavily on statistical analysis to analyze structured data for marketing insights. It is also common for firms to purchase third-party data and analysis, especially for external market and competitor analysis. Such analysis tends to be standardized across firms (with limited degree of customization), and thus the insights derived from it are less useful for deriving a unique value proposition.

How to Buy an AI Solution for Business The Right Way: 9 Questions New Customers Should Consider

The Secretary shall, as appropriate, consult with private AI laboratories, academia, civil society, and third-party evaluators, and shall use existing solutions. A key to delivering this vision will be an expansion of translational research in the field of healthcare applications of artificial intelligence. Alongside this, we need investment into the upskilling of a healthcare workforce and future leaders that are digitally enabled, and to understand and embrace, rather than being intimidated by, the potential of an AI-augmented healthcare system. Technologies such as artificial intelligence (AI) have already shown great potential to improve customer experience and engagement.

Evaluate Different AI Solutions and Providers

Because the translation can happen immediately (and without involving a human translator), the customer can experience more convenient and efficient support. AI helps navigate the agent through the interaction, offering the most relevant responses for the agent to use based on customer insights and context. According to Lauren Hakim, a product marketer at Zendesk, proactive engagement is one of the most effective uses for AI-powered chatbots.

Advancements in technology and the adoption of AI have rapidly changed the landscape of customer service. As businesses continue to prioritize providing a better customer experience, AI tools for customer service have emerged as essential solutions for delivering efficient and effective support. Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually. If you’ve ever adopted a new system, tool, or strategy in your business, you already know it can be a lengthy and complex process.

Accelerate productivity

Take into consideration the end-to-end requirements during your planning phase as getting the right skillset—whether it is building your own or utilizing outside expertise like consultants—will

take time and impact your project delivery timelines. To help determine if AI is the right choice for your company and your business problem, CompTIA’s Artificial Intelligence Advisory Council developed questions and answers that business decision-makers and AI practitioners should consider before investing in AI. While both decision-makers and practitioners have their own points to consider, it’s recommended that they work in tandem

to make the best, most appropriate decision for their respective environments. Additionally, consider the team’s ability to provide ongoing support and updates. AI is a rapidly evolving field, and the product you choose should have a team that can adapt and improve the solution over time. Finally, you should review and update your AI tools and platforms regularly to ensure that they are meeting your expectations and adapting to your changing needs.

The hardest part about creating software is not writing code—it’s creating the requirements, and those software requirements are still defined by humans. Salesforce reports that the number of customer service leaders using AI has increased by 88% since 2020. Gartner reports that customer service budgets are directing increased spend to tech solutions. Many businesses are already utilizing screen monitoring, recording keystrokes, and implementing facial recognition programs that scan workers’ faces several times a day. Durable asks a few simple questions like “What type of a business you’re building?

You will find that most AI-powered tools and websites for sale on Flippa fall in the industry AI category. That’s why in this article, we’ll go through the key questions you need to ask yourself when considering an AI product purchase. However, just like Arthur Eichele and the lion, it can be difficult to tell whether AI products work as advertised at a glance.

  • In the digitally connected world, market data can be easily tracked and monitored.
  • Every tee includes a QR code on the tag so that customers can return the shirt to Teemill.
  • Statistics from an impact report focused on Wing’s potential in Dallas, Texas, show that drone delivery could drive $26,000 in revenue gains for businesses per year.
  • It can include personal selling, traditional mass media advertising, and more commonly nowadays direct marketing, database marketing, and digital marketing (social media marketing, mobile marketing, search engine optimization, etc.).
  • It becomes very important for a business to evaluate the long-term cost of an AI solution, it is also very important to evaluate its value towards the maintenance cost.

No AI model, be it a statistical machine learning model or a natural language processing model, will be perfect on day one of deployment. Therefore, it is imperative that the overall

AI solution provide mechanisms for subject matter experts to provide feedback to the model. AI models must be retrained often with the feedback provided for correcting and improving. Carefully analyzing and categorizing errors goes a long way in determining

where improvements are needed. Data often resides in multiple silos within an organization in multiple structured (i.e., sales, CRM, ERP, HRM, marketing, finance, etc.) or unstructured (i.e., email, text messages, voice messages, videos, etc.) platforms. Depending on the size and scope

of your project, you may need to access multiple data sources simultaneously within the organization while taking data governance and data privacy into consideration.

Data requirements

That is just one of the benefits of machine learning algorithms, which, along with artificial intelligence, are allowing companies to tap into volumes of information generated across their business units, partners and third-party sources. For firms that embrace a theory-driven approach to marketing strategies, data and intelligences resulting from this stage play a critical role. Chat GPT-powered conversational AI can help e-commerce businesses provide personalized and natural customer interactions.

The success of artificial intelligence tools is heavily dependent on the quality and quantity of data it receives. Therefore, it’s important to gather and prepare data before you start building AI models. Implementing AI is a complex process that requires careful planning and consideration. Organizations must ensure that their data is of high quality, define the problem they want to solve, select the right AI model, integrate the system with existing systems, and consider ethical implications. By considering these key factors, organizations can build a successful AI implementation strategy and reap the benefits of AI.


Yet accurately predicting demand is only getting more challenging because historical sales data are no longer enough, even when combined with seasonal data. AI can also allow you to keep track of all your products throughout the entire supply chain, from the manufacturer to your store, not just those already in it. Using a tracking system with integrated RFID and GPS technology, you can locate your products or supplies and even monitor in what conditions they are stored or transported. Like many industries, logistics can significantly benefit from using AI-powered or “smart” devices and automating various repetitive tasks.

  • A prominent example of this is taking place in stock exchanges, where high-frequency trading by machines has replaced much of human decisionmaking.
  • The content creation part of promotion, though having a lower degree of automation, is increasingly handled by thinking AI, such as AI writers, to generate content on its own, or to stimulate human creativity.
  • Unless you’ve been living under a rock for the last couple years – or at least since the launch of ChatGPT in October 2022 – then you’ve already heard about how artificial intelligence (AI) has become essential for any organization that doesn’t want to get left behind in the dust.
  • They are combining 5G and AI in order to locate machines, cars, tools, and parts within the factory in real-time.
  • You can offer customer support even during weekends and holidays with AI-powered chatbots.

Read more about How to Buy an AI Solution for Business The Right Questions New Customers Should Consider here.