Gen AI in manufacturing: Finding value with Process Intelligence

Almost a third of developers think generative AI is a negative for the games industry, says new survey

The key to all usage of generative AI is to stay on your toes, keep your wits about you, and always challenge and double-check anything the AI emits. I am betting that you would like to see an example of how generative AI enters this realm. You might find it of keen interest that ChatGPT garners a whopping 300 million weekly active users. The issue though is that finding someone willing to spend the time to do so might be difficult.

Still, the ways consumers are choosing to use their mobile devices are beginning to shift. The report shows growth in app categories that connect users across devices or to in-person experiences. Food and drink app downloads, for instance, increased 8.5% YoY as more companies integrated their app into the overall customer experience. Similarly, finance apps, led by the popularity of digital wallets and mobile banking, saw 8% YoY download growth to 7.5 billion and a 21% increase in time spent.

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The use of icebreakers is a common social mechanism that can be used with people that you’ve newly met. A lousy icebreaker could land like a dud and forever leave a foul impression on the other person. “A lot of the analytics in organizations requires users to have an understanding of the data they’re looking at,” she said. Organizations are using GenAI to innovate — whether that’s to create new products and services or to find new ways to differentiate themselves in the market, Wong said.

She previously co-founded Wharton Interactive, an initiative at the Wharton School that has scaled globally. Sarah has been on the Executive Committee of Reimagine Education for almost a decade, and was one of the ten globally to be selected as an AWS Education Champion. Her other great passion is supporting organizations who work to attract and promote women and girls into STEM. GenAI is a hyper-complex technology and prone to black-box internal mechanisms that make its decision-making hard to scrutinize. Its ability to generate seemingly endless creative results and outputs means that bias can surface in different ways, some obvious and some less so.

It is the publisher of Search Engine Land, the leading digital publication covering the latest search engine optimization (SEO) and pay-per-click (PPC) marketing news, trends and advice. These capabilities empower developers to seamlessly transition from development to production, ensuring reliability and scalability at every stage. At the center of Llama Stack’s design is its commitment to providing a consistent and versatile developer experience. The platform offers a one-stop solution for building production-grade applications, supporting APIs covering inference, Retrieval-Augmented Generation (RAG), agents, safety, and telemetry.

GenAI vs. ML Technology

Additionally, project managers who specialize in AI-driven project management may find themselves at the forefront of innovation, leading cutting-edge projects that shape the future of their industries [3]. Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of project management, significantly influencing the roles and careers of project managers. The integration of GenAI into project management processes presents a compelling opportunity for project managers to enhance their productivity, efficiency, and overall project success [4]. Despite these differences, both GenAI and ML hold transformative potential for enterprises, offering opportunities to increase revenue, reduce costs, improve productivity, and better manage risks[4]. As the technology continues to evolve, the distinctions between GenAI and ML may blur, but their unique capabilities will undoubtedly continue to drive innovation across various sectors. Around two years ago, the world was inundated with news about how generative AI or large language models would revolutionize the world.

Once these challenges are overcome, however, the next phase of AI adoption will likely focus on more sophisticated applications that continue to transform IT operations. Nearly half of the respondents (49%) said data is scattered across multiple repositories, and 45% reported that their existing solutions focus on simple, reactive use cases that don’t tap into AI’s full capabilities. It is highly recommended reading to fully understand some of the concepts that follow. If you’re a software developer right now, it is nearly impossible to avoid chatter about generative AI.

In the generation stage, both the user’s query and the retrieved information are used to prompt the model to generate content. What used to be an optimum process yesterday can lead to inefficiencies today in a rapidly changing industrial landscape. In this case companies must identify product lines to justify automation of processes or zero-in on the best solution for a process. The case study in the second part of this series emphasized how companies can utilize Process Intelligence to optimize their processes to gain additional value and improve their cash flow.

At least as far as YouTube is concerned, the worst offenders of AI plagiarism work by downloading the video’s subtitles, passing them through some sort of AI model, and then generating another YouTube video based off of the original creator’s work. Most subtitle files are the fairly straightfoward .srt filetype which only allows for timing and text information. But a more obscure subtitle filetype known as Advanced SubStation Alpha, or .ass, allows for all kinds of subtitle customization like orientation, formatting, font types, colors, shadowing, and many others. YouTuber [f4mi] realized that using this subtitle system, extra garbage text could be placed in the subtitle filetype but set out of view of the video itself, either by placing the text outside the viewable area or increasing its transparency. So now when an AI crawler downloads the subtitle file it can’t distinguish real subtitles from the garbage placed into it.

Democratized Data-Driven Decisions

Critics argue that AI lacks true creativity, as it operates without consciousness or intent. The most innovative results often stem from human-AI collaboration, where AI amplifies human creativity rather than replacing it. AI enables the dynamic creation of new levels, quests, and events, responding to player behavior. Procedural Content Generation (PCG) ensures high replayability, while Dynamic Event Generation offers personalized gameplay based on player choices.

• Alibaba Cloud has seen substantial growth, with double-digit increases in public cloud services and triple-digit growth in AI-related product revenue for the fifth consecutive quarter, according to its latest earnings.
• Generative AI can analyze large volumes of data to create personalized advertisements, design visuals, and generate copy that resonates with consumers.
• Finally, in September of 2020, Robust Intelligence landed the first sale of their AI firewall product to Expedia after a cold outreach on LinkedIn.
• This process allows the RAG system to extract structured, relevant knowledge efficiently and leverage it to provide clear diagnostic explanations14.
• This versatility ensures that applications can be scaled up or down based on specific needs.

Meanwhile, developers are saying building generative AI applications is too hard, especially with the immature tooling they have to work with. The use of GenAI to produce deepfakes, or manipulated media designed to appear outwardly authentic, has significant implications for women’s engagement in politics. For example, malign actors can create synthetic content spreading provocative falsehoods which undercut female elected officials.

But one eye-opening slide shows who is adopting AI tools in game development—and for the most part, it’s not the people programming or creating assets for games. The heaviest AI use showed up for those working in “Business & Finance,” at 50 percent of respondents. Next up were those in “Production & Team Leadership” and “Community, Marketing, & PR” at 40 percent of respondents each. Specifically, developers are much more pessimistic about tools like Copilot, ChatGPT, and MidJourney than they were the prior year.

Transparency enhancement

Expedia’s VP of data science was sold on the promise of how Robust’s product could be used for quality assurance, perhaps even more than its value as a security tool. At the time, he had a lot of PhDs in his organization who were terrific researchers but lacked coding expertise, and he believed Robust’s technology could help identify the issues in the models they were building. Expedia was willing to partner with Robust despite their product’s nascency because there was simply nothing else like it in the market. As the adoption of generative AI continues to expand, developers face mounting challenges in building and deploying robust applications. The complexity of managing diverse infrastructure, ensuring compliance and safety, and maintaining flexibility in provider choices has created a pressing need for unified solutions.

Weekly summaries based on meeting notes generated by GenAI, for instance, ensure that team members are consistently aligned without expending additional effort on documentation[5]. GenAI tools have revolutionized task management by intelligently assigning tasks, predicting potential bottlenecks, and suggesting optimal workflows. For example, AI-powered tools can import current workflows, break down complex projects, and plot them on a roadmap, thereby helping project managers determine realistic time frames for project completion[5]. This dynamic and responsive planning is critical in Agile environments where adaptability and swift responses to change are paramount.

Over half of executives believe that generative AI aids in better allocation of resources, capacity, talent, or skills, which is essential for maintaining robust cybersecurity operations[4]. Despite its powerful capabilities, it’s crucial to employ generative AI to augment, rather than replace, human oversight, ensuring that its deployment aligns with ethical standards and company values [5]. In a novel approach to cyber threat-hunting, the combination of generative adversarial networks and Transformer-based models is used to identify and avert attacks in real time. This methodology is particularly effective in intrusion detection systems (IDS), especially in the rapidly growing IoT landscape, where efficient mitigation of cyber threats is crucial[8]. The rapid development of generative AI (Gen AI) has prompted a wave of enterprise experimentation over the past year, with companies across sectors testing applications from content creation to software development. Yet the path from pilot projects to full-scale deployment presents significant challenges, according to new research from Deloitte.

This approach often involves the use of neural networks and supervised learning techniques, which are essential for training algorithms to recognize patterns indicative of cyber threats. However, the application of neural networks also introduces challenges, such as the need for explainability and control over algorithmic decisions[14][1]. Addressing these challenges requires proactive measures, including AI ethics reviews and robust data governance policies[12].

However, traditional means for continuous improvements like Lean Six Sigma and KPIs cannot visualize the actual process execution, predict the scale of potential improvements, or identify the root cause of inefficient processes. As highlighted through the case studies in the first part of this series, generative AI in solutions like QAD Process Intelligence can help companies uncover their inefficiencies and resolve these issues. Looking forward, generative AI’s ability to streamline security protocols and its role in training through realistic and dynamic scenarios will continue to improve decision-making skills among IT security professionals [3].

Survey: College students enjoy using generative AI tutor – Inside Higher Ed

Survey: College students enjoy using generative AI tutor.

Posted: Wed, 22 Jan 2025 08:01:50 GMT [source]

GenAI excels at reducing the time project managers spend on repetitive tasks, freeing them up to focus on higher-level activities such as critical thinking and problem-solving[9]. For example, generative AI can produce automated reports and perform complex data analyses, thus ensuring that project managers have up-to-date and accurate information at their fingertips [4]. This automation not only enhances efficiency but also reduces the likelihood of human error, contributing to better project outcomes [9]. Generative AI (GenAI) is revolutionizing the field of project management by automating numerous routine tasks, thus enabling project managers to concentrate on strategic aspects and overall project output. This transformative technology has the potential to significantly enhance efficiency by handling time-consuming activities such as moving cards on a board and drafting summaries, which are typically seen as nuisance-like tasks[5]. The transformative potential of generative AI lies in the rapid evolution of foundation models, which are trained on broad data sets and can be adapted to perform a wide range of tasks.

The data used to train these models can perpetuate existing biases, raising questions about the trustworthiness and interpretability of the outputs [5]. This is particularly problematic in cybersecurity, where impartiality and accuracy are paramount. GenAI is also a building component for agentic AI systems to support even more automated decision-making. Agentic AI systems can act autonomously to solve multistep problems in real time, determine the right actions to take and then take those actions to accomplish the desired outcome.

• When used creatively, GenAI can allow female candidates to get their message out when more traditional options are cost prohibitive.
• These technologies often rely on models such as generative adversarial networks (GANs) and artificial neural networks (ANNs), which have shown considerable success in identifying and responding to cyber threats.
• As AI becomes more integrated into game creation, questions arise about ownership rights and the need for updated legislation.
• Financial services are no strangers to advances in technology, but generative AI presents an uncharted and complex landscape for the industry.

In 2024, 21 percent of game developers thought generative AI would have a “positive” impact on the video game industry. Today the CMSWire community consists of over 5 million influential customer experience, customer service and digital experience leaders, the majority of whom are based in North America and employed by medium to large organizations. Generative artificial intelligence has become more common in education technology tools, including textbooks and learning management systems. Generative AI models are trained on massive datasets, often containing millions of works. While individual pieces may contribute minimally, the sheer scale of usage complicates the argument for fair use. Fair use traditionally applies to specific, limited uses—not wholesale ingestion of copyrighted content on a global scale.

Despite their increasing success, Singer could see that LLMs were scaling faster than his company possibly could. In order to have the impact he aspired to, he would have to partner with a larger company. So in the summer of 2024, after receiving three other offers, Singer accepted Cisco’s $400M bid to acquire Robust Intelligence.

“Amid the promise of AI agents and the evolution of foundational models, future-thinking organisations are as bullish as ever in building bridges to ROI, all while understanding the need for nuance — and patience — as we embrace this next wave of GenAI. We are seeing a shift as leaders move past the initial hype to strategically deploying GenAI in the core of their businesses. Focus is essential, prioritising demonstrated use cases with measurable return on investment,” said Joe UCUZOGLU, Chief Executive Officer, Deloitte Global. Proceed with caution and rely on the predictability that the digital landscape is unpredictable.