AI Insights
How automation is using the latest technology across various sectors
A majority of small businesses are using artificial intelligence and finding out it can save time and money.
Artificial Intelligence and automation are often used interchangeably. While the technologies are similar, the concepts are different. Automation is often used to reduce human labor for routine or predictable tasks, while A.I. simulates human intelligence that can eventually act independently.
“Artificial intelligence is a way of making workers more productive, and whether or not that enhanced productivity leads to more jobs or less jobs really depends on a field-by-field basis,” said senior advisor Gregory Allen with the Wadhwani A.I. center at the Center for Strategic and International Studies. “Past examples of automation, such as agriculture, in the 1920s, roughly one out of every three workers in America worked on a farm. And there was about 100 million Americans then. Fast forward to today, and we have a country of more than 300 million people, but less than 1% of Americans do their work on a farm.”
A similar trend happened throughout the manufacturing sector. At the end of the year 2000, there were more than 17 million manufacturing workers according to the U.S. Bureau of Labor statistics and the Federal Reserve Bank of St. Louis. As of June, there are 12.7 million workers. Research from the University of Chicago found, while automation had little effect on overall employment, robots did impact the manufacturing sector.
“Tractors made farmers vastly more productive, but that didn’t result in more farming jobs. It just resulted in much more productivity in agriculture,” Allen said.
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Researchers are able to analyze the performance of Major League Baseball pitchers by using A.I. algorithms and stadium camera systems. (University of Waterloo / Fox News)
According to our Fox News Polling, just 3% of voters expressed fear over A.I.’s threat to jobs when asked about their first reaction to the technology without a listed set of responses. Overall, 43% gave negative reviews while 26% reacted positively.
Robots now are being trained to work alongside humans. Some have been built to help with household chores, address worker shortages in certain sectors and even participate in robotic sporting events.
The most recent data from the International Federation of Robotics found more than 4 million robots working in factories around the world in 2023. 70% of new robots deployed that year, began work alongside humans in Asia. Many of those now incorporate artificial intelligence to enhance productivity.
“We’re seeing a labor shortage actually in many industries, automotive, transportation and so on, where the older generation is going into retirement. The middle generation is not interested in those tasks anymore and the younger generation for sure wants to do other things,” Arnaud Robert with Hexagon Robotics Division told Reuters.
Hexagon is developing a robot called AEON. The humanoid is built to work in live industrial settings and has an A.I. driven system with special intelligence. Its wheels help it move four times faster than humans typically walk. The bot can also go up steps while mapping its surroundings with 22 sensors.
ARTIFICIAL INTELLIGENCE FUELS BIG TECH PARTNERSHIPS WITH NUCLEAR ENERGY PRODUCERS
Researchers are able to create 3D models of pitchers, which athletes and trainers could study from multiple angles. (University of Waterloo)
“What you see with technology waves is that there is an adjustment that the economy has to make, but ultimately, it makes our economy more dynamic,” White House A.I. and Crypto Czar David Sacks said. “It increases the wealth of our economy and the size of our economy, and it ultimately improves productivity and wages.”
Driverless cars are also using A.I. to safely hit the road. Waymo uses detailed maps and real-time sensor data to determine its location at all times.
“The more they send these vehicles out with a bunch of sensors that are gathering data as they drive every additional mile, they’re creating more data for that training data set,” Allen said.
Even major league sports are using automation, and in some cases artificial intelligence. Researchers at the University of Waterloo in Canada are using A.I. algorithms and stadium camera systems to analyze Major League Baseball pitcher performance. The Baltimore Orioles joint-funded the project called Pitchernet, which could help improve form and prevent injuries. Using Hawk-Eye Innovations camera systems and smartphone video, researchers created 3D models of pitchers that athletes and trainers could study from multiple angles. Unlike most video, the models remove blurriness, giving a clearer view of the pitcher’s movements. Researchers are also exploring using the Pitchernet technology in batting and other sports like hockey and basketball.
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Overview of a PitcherNet System graphics analyzing a pitcher’s baseball throw. (University of Waterloo)
The same technology is also being used as part of testing for an Automated Ball-Strike System, or ABS. Triple-A minor league teams have been using the so-called robot umpires for the past few seasons. Teams tested both situations in which the technology called every pitch and when it was used as challenge system. Major League Baseball also began testing the challenge system in 13 of its spring training parks across Florida and Arizona this February and March.
Each team started a game with two challenges. The batter, pitcher and catcher were the only players who could contest a ball-strike call. Teams lost a challenge if the umpire’s original call was confirmed. The system allowed umpires to keep their jobs, while strike zone calls were slightly more accurate. According to MLB, just 2.6% of calls were challenged throughout spring training games that incorporated ABS. 52.2% of those challenges were overturned. Catchers had the highest success rate at 56%, followed by batters at 50% and pitchers at 41%.
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Triple-A announced last summer it would shift to a full challenge system. MLB commissioner Rob Manfred said in June, MLB could incorporate the automated system into its regular season as soon as 2026. The Athletic reports, major league teams would use the same challenge system from spring training, with human umpires still making the majority of the calls.
Many companies across other sectors agree that machines should not go unsupervised.
“I think that we should always ensure that AI remains under human control,” Microsoft Vice Chair and President Brad Smith said. “One of first proposals we made early in 2023 was to insure that A.I., always has an off switch, that it has an emergency brake. Now that’s the way high-speed trains work. That’s the way the school buses, we put our children on, work. Let’s ensure that AI works this way as well.”
AI Insights
Mapping the application of artificial intelligence in traditional medicine: technical brief – World
WHO, ITU, WIPO showcase a new report on AI use in traditional medicine
Artificial intelligence (AI) is ushering in a transformative era for traditional medicine, one where centuries-old healing systems are enhanced by cutting-edge technologies to deliver more safe, personalized, effective, and accessible care.
At the AI for Good Global Summit, the World Health Organization (WHO), the International Telecommunication Union (ITU), and the World Intellectual Property Organization (WIPO) released a new technical brief, Mapping the application of artificial intelligence in traditional medicine. Launched under the Global Initiative on AI for Health, this brief offers a roadmap harnessing this potential responsibly while safeguarding cultural heritage and data sovereignty.
A new era for traditional medicine
Traditional, complementary and integrative medicine (TCIM) is practiced in 170 countries and is used by billions of people. The TCIM practices are increasingly popular globally, driven by a growing interest in holistic health approaches that emphasize prevention, health promotion and rehabilitation.
The new brief showcases experiences in many countries using AI to unlock new frontiers in personalized care, drug discovery, and biodiversity conservation. It includes examples such as how AI-powered diagnostics are being used in Ayurgenomics; machine learning models identifying medicinal plants in countries including Ghana and South Africa; and the use of AI to analyze traditional medicine compounds to treat blood disorders in the Republic of Korea.
“Our Global Initiative on AI for Health aims to help all countries benefit from AI solutions and ensure that they are safe, effective, and ethical,” said Seizo Onoe, Director of the ITU Telecommunication Standardization Bureau. “This partnership of ITU, WHO and WIPO brings together the essential expertise.”
Data-driven innovation with ethical roots
The brief emphasizes the importance of good-quality, inclusive data and participatory design to ensure AI systems reflect the diversity and complexity of traditional medicine. AI applications can support strengthening the evidence and research base for TCIM, for example through the Traditional Knowledge Digital Library in India and the Virtual Health Library in the Americas, which use AI to preserve Indigenous knowledge, promote collaboration and prevent biopiracy. Biopiracy is a term for unauthorized extraction of biological resources and/or associated traditional knowledge from developing countries or the patenting of spurious inventions based on such knowledge or resources without compensation.
“Intellectual property is an important tool to accelerate the integration of AI into traditional medicine,” said WIPO Assistant Director- General, Edward Kwakwa. “Our work at WIPO, including the recently adopted WIPO Treaty on Intellectual Property, Genetic Resources and Associated Traditional Knowledge, supports stakeholders manage IP to deliver on policy priorities including for Indigenous Peoples as well as local communities.”
Guarding data sovereignty, empowering communities
The new document calls for urgent action to uphold Indigenous Data Sovereignty (IDSov) and ensure that AI development is guided by free, prior, and informed consent (FPIC) principles. It showcases community-led data governance models from Canada, New Zealand, and Australia, and urges governments to adopt legislation that empowers Indigenous Peoples to control and benefit from their data.
“AI must not become a new frontier for exploitation,” said Dr Yukiko Nakatani, WHO Assistant Director-General for Health Systems. “We must ensure that Indigenous Peoples and local communities are not only protected but are active partners in shaping the future of AI in traditional medicine.”
A global call to action
With the global TCIM market projected to reach nearly US$600 billion in 2025, the application of AI could further accelerate the growth and impact of TCIM and holistic health care. Current utilization and potential of AI highlight many opportunities, but there are many areas of knowledge gaps and risks.
There is a need to develop holistic frameworks tailored to TCIM in areas such as regulation, knowledge sharing, capacity building, data governance and the promotion of equity, to ensure the safe, ethical and evidence-based integration of frontier technologies such as AI into the TCIM landscape.
The new technical brief calls on all stakeholders to:
- Invest in inclusive AI ecosystems that respect cultural diversity and IDSov;
- Develop national policies and legal frameworks that explicitly address AI in traditional medicine;
- Build capacity and digital literacy among traditional medicine practitioners and communities;
- Establish global standards for data quality, interoperability, and ethical AI use; and
- Safeguard traditional knowledge through AI-powered digital repositories and benefit-sharing models.
By aligning the power of AI with the wisdom of traditional medicine, a new paradigm of care can emerge; one that honors the past, empowers the present, and shapes a healthier, more equitable future for all.
AI Insights
Fraud experts warn of smishing scams made easier by artificial intelligence, new tech – Toronto Star
AI Insights
Grok 4 Overview : Pricing, Features, Benefits and Limitations
What if the future of artificial intelligence wasn’t just about answering questions or generating content, but truly understanding the world as we do? Enter Grok 4, a new advancement in artificial general intelligence (AGI) developed by XAI. Unlike its predecessors or competitors, Grok 4 doesn’t just process information—it reasons, adapts, and excels across disciplines like mathematics, science, and complex problem-solving. With a staggering ability to handle a 256k token context window and multimodal inputs ranging from text to images, Grok 4 is redefining what it means to be an intelligent system. Yet, as with any innovation, its brilliance comes with challenges, from steep subscription costs to areas where its performance still lags. The question remains: is Grok 4 the AI revolution we’ve been waiting for, or just another step along the way?
In this exploration of Grok 4, World of AI uncover the features that set it apart, from its postgraduate-level reasoning abilities to its enterprise-grade security and real-time data search capabilities. You’ll discover how its multimodal design positions it as a versatile tool for industries like healthcare, finance, and research, while its unique training methodology ensures adaptability and precision. But we won’t stop there—this deep dive will also examine its limitations, pricing structure, and the ambitious updates on the horizon, such as coding enhancements and video generation models. Whether you’re an enterprise leader seeking innovative solutions or a curious mind exploring the frontier of AGI, Grok 4 offers a fascinating glimpse into the evolving landscape of intelligent systems.
Grok 4 AGI Breakthrough
TL;DR Key Takeaways :
- Grok 4, developed by XAI, sets a new standard in artificial general intelligence (AGI) with superior performance in reasoning, mathematics, science, and tool utilization, surpassing competitors like Gemini 2.5 and Claude 4.
- Its 256k token context window, double that of its predecessor, enables advanced data analysis, long-form content generation, and complex problem-solving, making it highly efficient for intricate tasks.
- Multimodal capabilities allow Grok 4 to process text, code, and images, making it versatile for industries such as healthcare, finance, and research, where precision and adaptability are critical.
- Key features include real-time data search, structured outputs, function calling, and enterprise-grade security, making sure seamless integration into workflows and robust data protection.
- Despite its high subscription costs and limitations in coding and UI mockups, planned updates like a dedicated coding model and video generation capabilities aim to enhance its functionality and maintain its leadership in AGI innovation.
What Sets Grok 4 Apart
Grok 4’s performance is unparalleled across a variety of disciplines. It demonstrates postgraduate-level intelligence in reasoning, mathematics, and science, excelling in rigorous benchmarks such as ARC AGI2 and HLE. These evaluations underscore its ability to outperform competitors by significant margins, showcasing its advanced problem-solving and analytical capabilities.
One of the most notable features of Grok 4 is its ability to process a 256k token context window, which is double the capacity of its predecessor, Grok 3. This expanded context window allows it to manage complex tasks with greater depth and efficiency, making it an indispensable tool for addressing intricate challenges. By using this capability, Grok 4 is particularly adept at handling large-scale data analysis, long-form content generation, and multifaceted problem-solving scenarios.
Multimodal Capabilities and Practical Applications
Grok 4’s multimodal capabilities enable it to process text, code, and image inputs, making it a highly versatile tool. This flexibility allows it to adapt seamlessly to a wide range of applications, from advanced problem-solving to dynamic workflows. Its design supports real-world reasoning and planning, which is particularly valuable for industries requiring precision, adaptability, and contextual understanding.
In practical terms, Grok 4 is well-suited for applications in industries such as:
- Healthcare: Assisting in medical research, diagnostics, and patient data analysis.
- Finance: Enhancing risk assessment, fraud detection, and financial modeling.
- Research and Development: Accelerating innovation through data analysis and hypothesis testing.
These capabilities make Grok 4 an essential tool for organizations aiming to streamline operations and improve decision-making processes.
Deep Dive into Grok 4
Here is a selection of other guides from our extensive library of content you may find of interest on Grok.
Innovative Training Methodology
Grok 4 employs a unique training methodology that combines reinforcement learning with pre-training. This dual approach enhances its ability to adapt to new tasks and environments while maintaining a robust foundational knowledge base. By integrating these techniques, Grok 4 achieves a level of contextual understanding and reasoning that distinguishes it from other models.
The reinforcement learning component allows Grok 4 to refine its decision-making processes through iterative feedback, while pre-training ensures a comprehensive grasp of diverse subjects. This combination not only improves its performance in specific tasks but also enhances its general adaptability, making it a reliable choice for both specialized and broad-spectrum applications.
Key Technical Features
Grok 4 introduces several advanced features designed to meet the needs of both enterprise and individual users. These include:
- Real-time data search: Enables dynamic and up-to-date information retrieval, making sure relevance and accuracy.
- Structured outputs and function calling: Assists seamless integration into complex workflows, enhancing operational efficiency.
- Enterprise-grade security: Provides robust data protection and ensures compliance with corporate standards, making it a trusted solution for sensitive applications.
These features make Grok 4 particularly valuable for industries where precision, security, and adaptability are critical. Its ability to integrate into existing systems and workflows further enhances its appeal as a versatile and reliable AI solution.
Pricing and Accessibility
Grok 4 is available through two subscription tiers, catering to different user needs:
- Super Grok: Priced at $300 per year, this tier offers access to Grok 4’s core capabilities.
- Super Grok Heavy: Priced at $3,000 per year, this tier provides enhanced features and higher usage limits for enterprise users.
For API access, the pricing structure is $3 per 1 million input tokens and $15 per 1 million output tokens. While these costs reflect the model’s advanced capabilities, they may pose a barrier for smaller organizations or individual users with limited budgets. However, for enterprises and professionals requiring innovative AI solutions, the investment is likely to yield significant returns in terms of efficiency and innovation.
Limitations and Future Developments
Despite its impressive capabilities, Grok 4 has certain limitations. It underperforms in areas such as coding and UI mockups, where some competitors currently excel. XAI has acknowledged these gaps and announced plans to address them in future updates. Upcoming developments include:
- A dedicated coding model to enhance programming-related tasks.
- A multimodal agent designed for more complex interactions.
- A video generation model, expanding its creative and multimedia capabilities.
These updates, expected to launch in October, aim to broaden Grok 4’s versatility and application scope, making sure it remains at the forefront of AGI innovation.
Benchmark Achievements
Grok 4 has achieved new results in AI benchmarks, nearly doubling the previous best scores on the ARC AGI2 leaderboard. It consistently outperforms leading models like Gemini 2.5 Pro and Claude 4 across various metrics, solidifying its position as a leader in the AGI field. These achievements underscore its advanced reasoning, problem-solving, and analytical capabilities, making it a standout choice for users seeking top-tier AI performance.
Looking Ahead
Grok 4 represents a significant milestone in the evolution of artificial general intelligence. Its advanced reasoning, multimodal capabilities, and enterprise-grade security make it a powerful tool for a wide range of applications. While its high costs and certain functional limitations may deter some users, its innovative features and planned updates position it as a frontrunner in the AI landscape. For enterprises seeking innovative solutions or individuals exploring the possibilities of AGI, Grok 4 offers a compelling glimpse into the future of intelligent systems.
Media Credit: WorldofAI
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