Archive 29.01.2025

At UC Berkeley, researchers in Sergey Levine's Robotic AI and Learning Lab eyed a table where a tower of 39 Jenga blocks stood perfectly stacked. Then a white-and-black robot, its single limb doubled over like a hunched-over giraffe, zoomed toward the tower, brandishing a black leather whip.

As AI evolves, effective collaboration across project lifecycles remains a pressing challenge for AI teams.

In fact, 20% of AI leaders cite collaboration as their biggest unmet need, underscoring that building cohesive AI teams is just as essential as building the AI itself. 

With AI initiatives growing in complexity and scale, organizations that foster strong, cross-functional partnerships gain a critical edge in the race for innovation. 

This quick guide equips AI leaders with practical strategies to strengthen collaboration across teams, ensuring smoother workflows, faster progress, and more successful AI outcomes. 

Teamwork hurdles AI leaders are facing

AI collaboration is strained by team silos, shifting work environments, misaligned objectives, and increasing business demands.

For AI teams, these challenges manifest in four key areas: 

• Fragmentation: Disjointed tools, workflows, and processes make it difficult for teams to operate as a cohesive unit.
  
  
• Coordination complexity: Aligning cross-functional teams on hand-off priorities, timelines, and dependencies becomes exponentially harder as projects scale.
  
  
• Inconsistent communication: Gaps in communication lead to missed opportunities, redundancies, rework, and confusion over project status and responsibilities.
  
  
• Model integrity: Ensuring model accuracy, fairness, and security requires seamless handoffs and constant oversight, but disconnected teams often lack the shared accountability or the observability tools needed to maintain it.
  

Addressing these hurdles is critical for AI leaders who want to streamline operations, minimize risks, and drive meaningful results faster.

Fragmentation workflows, tools, and languages

An AI project typically passes through five teams, seven tools, and 12 programming languages before reaching its business users — and that’s just the beginning.

Here’s how fragmentation disrupts collaboration and what AI leaders can do to fix it:

• Disjointed projects: Silos between teams create misalignment. During the planning stage, design clear workflows and shared goals.
  
  
• Duplicated efforts: Redundant work slows progress and creates waste. Use shared documentation and centralized project tools to avoid overlap.
  
  
• Delays in completion: Poor handoffs create bottlenecks. Implement structured handoff processes and align timelines to keep projects moving.
  
  
• Tool and coding language incompatibility: Incompatible tools hinder interoperability. Standardize tools and programming languages where possible to enhance compatibility and streamline collaboration.
  

When the processes and teams are fragmented, it’s harder to maintain a united vision for the project. Over time, these misalignments can erode the business impact and user engagement of the final AI output.

The hidden cost of hand-offs

Each stage of an AI project presents a new hand-off – and with it, new risks to progress and performance. Here’s where things often go wrong: 

• Data gaps from research to development: Incomplete or inconsistent data transfers and data duplication slow development and increases rework.
  
  
• Misaligned expectations: Unclear testing criteria lead to defects and delays during development-to-testing handoffs.
  
  
• Integration issues: Differences in technical environments can cause failures when models are moved from test to production.
  
  
• Weak monitoring:  Limited oversight after deployment allows undetected issues to harm model performance and jeopardize business operations.
  

To mitigate these risks, AI leaders should offer solutions that synchronize cross-functional teams at each stage of development to preserve project momentum and ensure a more predictable, controlled path to deployment. 

Strategic solutions

Breaking down barriers in team communications

AI leaders face a growing obstacle in uniting code-first and low-code teams while streamlining workflows to improve efficiency. This disconnect is significant, with 13% of AI leaders citing collaboration issues between teams as a major barrier when advancing AI use cases through various lifecycle stages.

To address these challenges, AI leaders can focus on two core strategies:

1. Provide context to align teams

AI leaders play a critical role in ensuring their teams understand the full project context, including the use case, business relevance, intended outcomes, and organizational policies. 

Integrating these insights into approval workflows and automated guardrails maintains clarity on roles and responsibilities, protects sensitive data like personally identifiable information (PII), and ensures compliance with policies.

By prioritizing transparent communication and embedding context into workflows, leaders create an environment where teams can confidently innovate without risking sensitive information or operational integrity.

2. Use centralized platforms for collaboration

AI teams need a centralized communication platform to collaborate across model development, testing, and deployment stages.

An integrated AI suite can streamline workflows by allowing teams to tag assets, add comments, and share resources through central registries and use case hubs.

Key features like automated versioning and comprehensive documentation ensure work integrity while providing a clear historical record, simplify handoffs, and keep projects on track.

By combining clear context-setting with centralized tools, AI leaders can bridge team communication gaps, eliminate redundancies, and maintain efficiency across the entire AI lifecycle.

Protecting model integrity from development to deployment

For many organizations, models take more than seven months to reach production – regardless of AI maturity. This lengthy timeline introduces more opportunities for errors, inconsistencies, and misaligned goals.  

To safeguard model integrity, AI leaders should:

• Automate documentation, versioning, and history tracking.
  
  
• Invest in technologies with customizable guards and deep observability at every step.
  
  
• Empower AI teams to easily and consistently test, validate, and compare models.
  
  
• Provide collaborative workspaces and centralized hubs for seamless communication and handoffs.
  
  
• Establish well-monitored data pipelines to prevent drift, and maintain data quality and consistency.
  
  
• Emphasize the importance of model documentation and conduct regular audits to meet compliance standards.
  
  
• Establish clear criteria for when to update or maintain models, and develop a rollback strategy to quickly revert to previous versions if needed.
  

By adopting these practices, AI leaders can ensure high standards of model integrity, reduce risk, and deliver impactful results.

Lead the way in AI collaboration and innovation

As an AI leader, you have the power to create environments where collaboration and innovation thrive.

By promoting shared knowledge, clear communication, and collective problem-solving, you can keep your teams motivated and focused on high-impact outcomes.

For deeper insights and actionable guidance, explore our Unmet AI Needs report, and uncover how to strengthen your AI strategy and team performance.

The post How to strengthen collaboration across AI teams appeared first on DataRobot.

This latest creation isn’t just one of the most sensitive and dexterous robotic hands yet created, but it’s sufficiently robust to endure impacts that have destroyed previous designs.

The rise of Industry 4.0 marks a turning point for businesses. It blends artificial intelligence (AI), the Internet of Things (IoT), and data science to transform operations and company growth. These technologies let businesses make smarter and faster decisions and […]

The post Adapting to Industry 4.0: Key Tech Innovations and Data Security for Today’s Businesses appeared first on TechSpective.

Recent technological advances have opened new possibilities for the development of assistive and medical tools, including prosthetic limbs. While these limbs used to be hard objects with the same shape as limbs, prosthetics are now softer and look more realistic, with some also integrating robotic components that considerably broaden their functions.

Can a computer learn a language the way a child does? A recent study sheds new light on this question. The researchers advocate for a fundamental revision of how artificial intelligence acquires and processes language.

Robots have come a long way since the Roomba. Today, drones are starting to deliver door to door, self-driving cars are navigating some roads, robo-dogs are aiding first responders, and still more bots are doing backflips and helping out on the factory floor. Still, Luca Carlone thinks the best is yet to come.

The higher the torque, the greater the ability to rotate heavy objects, overcome tough resistance or to transport objects vertically. Applications which rely on high torque can include hoists and cranes, as well as industrial mixing machines.

In a fascinating blend of technology and artistry, researchers present a study in PeerJ Computer Science, showcasing how humanoid robots can collaborate seamlessly with human musicians during live musical performances. This innovative work highlights the evolving role of robotics in entertainment and creativity.

Researchers at the University of Twente have developed CEINMS-RT, an open-source platform designed to transform the field of wearable robotics.

In the same way that terrestrial life evolved from ocean swimmers to land walkers, soft robots are progressing, too, thanks to recent research in battery development and design.

In the same way that terrestrial life evolved from ocean swimmers to land walkers, soft robots are progressing, too, thanks to recent Cornell research in battery development and design.

Scientists estimate that the energy consumption of artificial intelligence could increase to up to 134 terawatt hours by 2027. AI training also requires a considerable amount of water—training the GPT model is said to have consumed around 700,000 liters of cooling water.

How AI Can Help Local Governments in 2025?

Artificial intelligence has impacted every sector in the world, including governments. As AI technology progresses, it presents opportunities for local governments to increase efficiency and make more informed judgments.

In 2025, AI will assume the lead in managing complicated urban environments, allocating resources efficiently, and responding rapidly to residents. Local governments can employ AI to address long-standing concerns like public safety, transportation, and community engagement.

This article examines how artificial intelligence (AI) could help municipal governments run more efficiently, quickly, and successfully in the coming year.

Current State of AI Adoption in Local Government

Adoption of AI in local governments has been on the rise for the last few years. Some local governments started to explore the use of AI for better, if not even more sophisticated, solutions and service provisions to their locales. Cities like Barcelona and Singapore have adopted AI to grow as a smart city. The areas of focus for these projects are matters of traffic management, waste reduction, and energy efficiency.

Although AI for public services is gaining interest, there still are many barriers to implementing it wholesale. Budgetary constraints and lack of technical know-how and security issues with the data have hindered it so far. Most of the countries do not yet utilize their AI-aimed potentials in decision-making either. However, as there is a requirement for better services within public services, the influence of AI on municipal administration will accelerate by 2025.

Key Areas Where AI Can Help Local Government

AI has numerous mobile applications for helping local governments enhance their operations. Here are some important areas where AI can make a difference:

1. Automating Public Services

AI can automate public services, saving time and lowering errors. Chatbots, for example, can address frequent citizen questions around the clock. This minimizes the workload of government personnel. Virtual assistants can help people with tasks such as applying for licenses or paying taxes online. This improves service delivery times and efficiency for both citizens and government staff.

AI can, therefore, assist governments in better judgments toward making critical decisions by analyzing data and predicting problems. For example, AI can help identify which routes require freeways by analyzing data from traffic sensors. AI also predicts possible crimes, and it is quite possible for law enforcers to prepare ahead of an issue erupting. This proactive approach will help save money while improving public safety at the same time.

2. Resource Management

AI can help them manage resources such as water, waste, and energy. Artificial intelligence can, for example, detect water leaks and also reduce water system waste. AI-based systems assist in giving control over energy consumption in public buildings and consequently keeps reducing expenditures. These upgrades enable governments to function more sustainably while reducing the waste of resources.

3. Transport and Urban Planning.

Artificial intelligence is used to improve urban transport systems. It can control traffic lights and therefore enable decongestion. This can also be utilized in plotting routes for public means of transport based on commuters’ data. As such, transportation networks are tailored according to the needs of the populations. AI enhances people’s movement as well as around cities, hence making them habitable.

Improving Citizen Services and Engagement

AI-powered solutions can improve how local governments communicate with the public. Governments can use AI to create online public forums where residents can express their issues or leave comments. AI can monitor these discussions and flag pertinent issues as actionable.

Municipal governments can also employ AI to provide citizens with real-time information. AI can automatically send notifications depending on location and urgency, such as an emergency notice, forthcoming event, or road closure. Citizens can stay updated without having to seek information by receiving notifications directly to their phones.

Customization of public services is another benefit of artificial intelligence. Governments can provide services that fulfill certain needs (helping the elderly or those with impairments) by evaluating citizen data. Government services are therefore more widely available and inclusive to the whole community.

Artificial Intelligence in Security and Public Safety

This enhances safety and security for the public at large. The local governments can choose to use AI-based surveillance systems that will allow them to monitor the public space in real-time. They will pick on the suspicious activities and alert the authorities before things get out of hand. AI is helping law enforcement in analyses of crime trends, resource allocation, and even predicting hotspots of crimes.

Predictive policing is an embryonic field wherein artificial intelligence is doing much to influence it. It can help police authority to stop crimes before being committed by making use of historical crime data. At the same time, it has some obvious ethical questions related to privacy and justice. The local governments should ensure that the efforts of AI are transparent and not biased toward certain groups of people.

AI has another impact on disaster response. The capacity to foresee the path of natural disasters like hurricanes and floods is enabled by AI systems. This allows local authorities to prepare ahead of time, that would prevent infrastructural damage and protect citizens.

Increasing the Effectiveness of Government

AI can automatically deal with a number of routine administrative tasks which municipal governments face on a daily basis. Through the use of AI, a variety of clerical and data entry functions dealing with document processing and public records management are addressed. Government employees will then be able to deal with more complex issues that require human creativity. As a result, productivity as a whole improved.

Artificial intelligence can also be useful in budgeting and financial management. It may study the patterns of expenditure and predict future expenditures to help the local governments manage their resources well. Furthermore, AI-based systems may detect anomalies in a financial transaction that will reduce fraud and waste.

Challenges and Ethical Considerations.

While AI provides several benefits, its adoption in local governments presents hurdles and ethical concerns. One of the main concerns is data privacy. AI systems depend on vast volumes of data, which are frequently obtained from citizens. These data should also be kept safely by the respective local governments so the privacy of citizens, and breach of data, are avoided.

One ethical concern is bias in AI systems since AI algorithms only perform as well as the data used to train them. So, biased data can lead to unjust or discriminatory results. This could result in specific groups being unfairly targeted or excluded from services. Governments must ensure that their artificial intelligence systems are transparent and accountable, with clear criteria for decision-making.

The next concern is safety. People should be careful while using AI systems in areas like public service delivery and law enforcement. Local governments need to interact with the public, specifying the advantages of AI and being open about its application.

Future Trends and Opportunities for AI in Local Governments.

The importance of AI in municipal governments is expected to increase further in the coming years. More advanced AI technologies are expected to be adopted by 2024. Machine learning and natural language processing will become increasingly popular in fields like public health, urban planning, and environmental monitoring. These technologies will enable governments to make better-informed judgments and respond to issues in real-time.

One rising trend is the use of artificial intelligence (AI) in environmental sustainability. Governments may employ artificial intelligence to track pollution levels, manage natural resources, and build more sustainable cities. With climate change continuing to exert its impacts in the world, it will similarly play a key role in assisting cities’ adaptability and resilience with AI.

This will continue to develop with AI-powered platforms that allow citizens to communicate directly with government services. This would, therefore, encourage more individualized services and real-time contact. It would be important for the locality of governments to stay ahead of such advances in order to increase their productivity and improve citizens’ results.

Conclusion

AI is changing the way local governments work, like providing powerful tools to improve efficiency, increase citizen participation, and make better decisions. AI brings previously inconceivable solutions to local governments, such as automating public services, anticipating crime, and managing resources.

Though data privacy, bias, and public lack of trust issues do arise, they can be solved through careful and transparent AI technology use. The advancements of AI can be capitalized upon by local governments as AI matures, answer the needs of communities well into 2024 and beyond.

By intentionally engaging in AI, local governments can build a safer, smarter, more effective community for the betterment of all people.

 

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New AI Offers New Automation Opportunities for Writers

ChatGPT’s maker OpenAI has released new experimental software — dubbed ‘Operator’ — that enables users to create autonomous AI agents.

Theoretically, writers could use the software to program an AI agent that, for example, could research, write — and continuously update — any article on any subject by:

~Automatically engaging in initial research on the Web

~Scouting for quotes to go along with that research from blogs and press releases

~Auto-writing the article in a preferred writing style

~SEO-optimizing the article for easy discovery by search engines

~Periodically researching the Web for new developments in
the article’s story

~Continually rewriting the article as new developments in the article’s story occur

So far, the experimental software is only available to ChatGPT Pro users, who pay a cool $200/month for premium access to ChatGPT services and new features.

Key competitors to OpenAI — including Google, Microsoft and Anthropic — have already released similar agent-making software.

In other news and analysis on AI writing:

*U.S. Government Officials to Get Briefing on ‘Super Agents’ This Week: ChatGPT-maker CEO Sam Altman is slated to give top U.S. government officials a closed door briefing Jan. 30 on its PhD-level ‘super agents.’

Observes writer Duncan Riley: “That OpenAI’s offering is said to include Ph.D.-level super-agents might also suggest that OpenAI has taken the technology beyond being able to automate tasks through to something more.”

The meeting reflects the overall zeitgeist associated with the breakneck advancement of AI during the past two years, which according to many experts and casual observers, has been at once thrilling and terrifying.

*ChatGPT-Maker Frees-Up Microsoft’s Grip on Its Tech: OpenAI — which has been running ChatGPT on Microsoft servers — has cut a new deal with its partner, enabling it to use the servers of other companies to run its AI.

Essentially, the deal gives Microsoft first crack at providing server services for OpenAI — as long as Microsoft can handle the request.

Observes writer Sebastian Moss: “With OpenAI’s compute demands growing, that relationship has grown strained as the world’s second-largest cloud provider struggled to keep up.”

“Last year, OpenAI announced that it would also work with Oracle — albeit in partnership with Microsoft.”

*ChatGPT-Maker a Major Player in Trump-Championed $500 Billion Stargate Project: OpenAI has emerged as a key player in the Stargate Project — an initiative to designed to attract major investment for the rapid build-out of AI infrastructure in the U.S.

Observes writer Craig S. Smith: “Intended to soak-up global investment capital before China has a chance to do the same, the recently announced Stargate Project — with its ambitious $500 billion investment over four years — represents a seismic shift in the global AI race, not only in terms of scale but also in strategy and execution.

“The initiative – a joint venture between OpenAI, Oracle and Softbank announced by President Donald J. Trump — will be the biggest AI infrastructure project in the world.

“It underscores the United States’ intent to assert dominance in AI development, framing it as a contest not just of technology but of economic and geopolitical power.”

*China’s AI Looking to Eat ChatGPT’s Lunch for Pennies-on-the-Dollar: Chinese researchers have released a competitor to ChatGPT — dubbed DeepSeek — they say performs just as well as ChatGPT at a fraction of the cost.

Observes writer Radhika Rajkumar: “The cost differences it represents could shake up the industry.”

A similar inexpensive rival to ChatGPT was also recently created by researchers at UC Berkeley, according to Rajkumar.

*Look for More Research Help from AI in Popular Apps: Writers and others can expect more AI-powered research tools in their favorite apps, thanks to a tool for developers from Perplexity, dubbed ‘Sonar.’

Essentially, the new tool gives app makers access to the same AI search tech that has made the Perplexity chatbot an extremely powerful alternative for users looking to perform Web searches and generate Web search summaries.

Observes writer Michael Nunez: “Zoom has already integrated Sonar into its AI Companion 2.0 product, allowing users to access real-time information without leaving video conferences.”

*Apple Kills Its AI News Summary Service: Smarting from glaring mistakes made by its AI news summary service, Apple has pulled the plug on the AI — at least for now.

One of the highest profile news media outlets disenchanted with Apple’s service is the BBC.

Earlier this month, Apple’s AI news summary service mistakenly reported that alleged CEO killer Luigi Mangione had shot himself — wrongly citing the BBC as the source of its summary.

Observes writer Tripp Mickle: “In a note to developers, Apple said it was working to improve summaries of notifications for news and entertainment apps.

“It plans to make the feature available again in a future software update.”

*Google Doubles Down In Its Race to Catch ChatGPT: Frustrated by ChatGPT’s dominance in the AI market, Google’s CEO Sundar Pichai is hoping to dominate the AI writer/tool by the close of 2025 with its own chatbot, Google Gemini.

Observes writer Miles Kruppa: “Google hasn’t said how many people currently use Gemini. But market leader ChatGPT has about 300 million weekly users.

“The Gemini app was the 54th most downloaded free app on iPhones Wednesday.

“ChatGPT was No. 4.”

*Look for Multi-Modal Search at Your Workplace in 2025: Google is predicting that increasing numbers of businesses will be offering enhanced search in 2025, which will enable you to input images, audio and video into your company search engine when doing research.

Other predictions in Google’s “5 Ways AI Will Shape Businesses in 2025” include:

~The rise of AI agents capable of completing autonomous tasks

~More AI on Web sites

~AI-enhanced cybersecurity

*AI Big Picture: China Militarizes AI Developed by Facebook Parent, Meta: Chinese researchers have modified open source AI software from Meta so that it can be used in warfare, according to writer Efosa Udinmwen.

Meta’s AI software — dubbed Llama — is free to download from the Web and has already been downloaded thousands of times.

Observes Udinmwen: “Meta, like other tech companies, has licensed Llama with clear restrictions against its use in military applications.

“However, as with many open-source projects, enforcing such restrictions is practically impossible.”

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–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

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