IBM Journey to AI blog

AUGUST 9, 2024

Many generative AI tools seem to possess the power of prediction. Conversational AI chatbots like ChatGPT can suggest the next verse in a song or poem. But generative AI is not predictive AI. Predictive AI use cases Predictive AI is mainly used in finance, retail, e-commerce and manufacturing.

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IBM Journey to AI blog

FEBRUARY 8, 2024

High-risk AI systems such as autonomous vehicles, medical devices and critical infrastructure (water, gas, electric, etc.) The EU AI Act also imposes rules as to how customers are notified when using a chatbot or when an emotion recognition system is used. Not complying with the EU AI Act can be costly: 7.5

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Unite.AI

APRIL 22, 2024

AI-Powered Virtual Consultations and Remote Monitoring Artificial Intelligence (AI) is transforming telehealth by enhancing its efficiency, accessibility, and personalization. AI-driven telehealth platforms, employing tools like chatbots, autonomously handle patient interactions, schedule appointments, and deliver medical information.

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IBM Journey to AI blog

AUGUST 12, 2024

The integration of generative AI, particularly LLMs, offers transformative potential to automate compliance processes, detect anomalies, and provide comprehensive insights into regulatory requirements. Financial institutions are prioritizing the integration of AI to address pressing challenges and enhance their competitive edge.

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Unite.AI

SEPTEMBER 13, 2023

Generative AI-powered chatbots could help alleviate much of the workload and preserve overextended patient access teams. On the patient side, generative AI has the potential to improve healthcare providers’ call center services.

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IBM Journey to AI blog

JULY 17, 2023

While traditional AI approaches provide customers with quick service, they have their limitations. Currently chat bots are relying on rule-based systems or traditional machine learning algorithms (or models) to automate tasks and provide predefined responses to customer inquiries. Watsonx.ai

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Marktechpost

MAY 12, 2024

It is based on adjustable and explainable AI technology. The technology provides automated, improved machine-learning techniques for fraud identification and proactive enforcement to reduce fraud and block rates. Fina also uses AI-based analytics to give users insights and recommendations for improving their financial strategy.

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ODSC - Open Data Science

AUGUST 14, 2023

“I still don’t know what AI is” If you’re like my parents and think I work at ChatGPT, then you may have to learn a little bit more about AI. Funny enough, you can use AI to explain AI. And now that you understand what AI is, it’s all about using it, leading to the next point. “No

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Unite.AI

OCTOBER 9, 2024

The rise of agentic AI—autonomous, AI-powered systems capable of reasoning and executing complex tasks without human intervention—marks a significant shift in enterprise technology. Providing clear documentation and explainable AI (XAI) frameworks that break down decision-making processes is essential.

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Dlabs.ai

JULY 19, 2023

It’s essential to keep humans involved in AI decision-making processes, especially when these decisions can significantly impact people’s lives. While AI systems can automate many tasks, they should not completely replace human judgment and intuition.

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ODSC - Open Data Science

FEBRUARY 18, 2025

Recent breakthroughs include OpenAIs GPT models, Google DeepMinds AlphaFold for protein folding, and AI-powered robotic assistants in industrial automation. These innovations enable AI to transition from tool-like applications to fully autonomous problem-solvers.

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ODSC - Open Data Science

JUNE 1, 2023

Generative AI May Help You Design Your New Game Character If legendary gaming studio Blizzard has its way, generative AI may be the next step in immersing in a game. Announcing the Free Generative AI Summit on July 20th To keep up with current trends, we’re hosting our first-ever Generative AI Summit, a free virtual event on July 20th.

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How to Learn Machine Learning

JANUARY 21, 2023

Algorithms are the instructions used by computers to process data and generate results, while machine learning is a type of AI that allows for machines to learn from experience, as well as external data sets. AI can be used to automate processes, make predictions, and provide decision support.

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Pickl AI

APRIL 8, 2024

Overhyped Expectations The media and tech companies often portray AI as a revolutionary technology capable of solving all our problems. This can lead to unrealistic expectations and disappointment when AI fails to live up to the hype. Example In 2016, a chatbot developed by Microsoft called Tay was launched on Twitter.

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Viso.ai

MARCH 26, 2024

2: Automated Document Analysis and Processing No.3: 2: Automated Document Analysis and Processing Computer vision can automate the extraction, analysis, and validation of document information. Real-world applications range from automating loan approvals to processing insurance claims. 1: Fraud Detection and Prevention No.2:

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Pickl AI

AUGUST 1, 2024

Deep Learning-powered chatbots can also assist in patient check-ins, answering questions, and providing support for chronic disease management. Operational Efficiency Deep Learning can optimise healthcare operations by automating administrative tasks, predicting patient flow, and optimising resource allocation.

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ODSC - Open Data Science

OCTOBER 10, 2024

Industry, Opinion, Career Advice AI Startups Fueling the Next Wave of AI Innovation at ODSC West These 5 AI startups will be a part of ODSC West later this month, and they’re worth paying attention to. Discover the cutting-edge innovation at ODSC West this October.

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Viso.ai

JULY 9, 2024

Global organizations like IKEA and DHL use it to build, deploy, and scale all computer vision applications in one place, with automated infrastructure. Understanding Generative AI Generative AI refers to the class of AI models capable of generating new content depending on an input. About us: viso.ai Get a personal demo.

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Pickl AI

JANUARY 13, 2025

It simplifies complex AI topics like clustering , dimensionality , and regression , providing practical examples and numeric calculations to enhance understanding. Key Features: Explains AI algorithms like clustering and regression. Key Features: Explores AIs impact on humanity. Discusses ethical challenges of AI.

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Pickl AI

NOVEMBER 18, 2024

Natural language processing ( NLP ) allows machines to understand, interpret, and generate human language, which powers applications like chatbots and voice assistants. Explainable AI (XAI) The demand for transparency in Machine Learning Models is growing. Let’s explore some of the key trends.

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IBM Journey to AI blog

SEPTEMBER 3, 2024

Data privacy issues Large language models (LLMs) are the underlying AI models for many generative AI applications, such as virtual assistants and conversational AI chatbots. Monitor AI model outputs for content that might expose your organization’s IP or infringe on the IP rights of others.

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The MLOps Blog

FEBRUARY 27, 2025

Picture this: youve spent months fine-tuning an AI-powered chatbot to provide mental health support. Transparency has become a key expectation in the AI industry, as highlighted by initiatives like the EU AI Act and guidelines from organizations such as the Partnership on AI, which emphasize the importance of explainable AI.

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Pickl AI

APRIL 2, 2024

Use it for sentiment analysis, topic modeling, and building chatbots. Computer Vision: Analyze visual data like images and videos to automate tasks, identify objects and patterns, and improve product development. Explainable AI (XAI): As AI models become more complex, there’s a growing need for interpretability.

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AI Weekly

AUGUST 22, 2024

wired.com A new ‘AI scientist’ can write science papers without any human input. theconversation.com AI Predicts Earthquakes With Unprecedented Accuracy Researchers at the University of Texas have developed an AI that predicted 70% of earthquakes during a trial in China, indicating potential for future quake risk mitigation.

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IBM Journey to AI blog

FEBRUARY 9, 2024

According to a recent IBM survey of over 1,000 employees at enterprise-scale companies , the top three factors driving AI adoption were advances in AI tools that make them more accessible, the need to reduce costs and automate key processes and the increasing amount of AI embedded into standard off-the-shelf business applications.

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