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The most capable model you can run on a single GPU or TPU.

Empa researchers are working on producing artificial muscles that can keep up with the real thing. They have now developed a method of producing the soft and elastic yet powerful structures using 3D printing.

Researchers are working on artificial muscles that can keep up with the real thing. They have now developed a method of producing the soft and elastic, yet powerful structures using 3D printing. One day, these could be used in medicine or robotics -- and anywhere else where things need to move at the touch of a button.

TL;DR:

Fragmented AI tools are draining  budgets, slowing adoption, and frustrating teams. To control costs and accelerate ROI, AI leaders need interoperable solutions that reduce tool sprawl and streamline workflows.

AI investment is under a microscope in 2025. Leaders aren’t just asked to prove AI’s value — they’re being asked why, after significant investments, their teams still struggle to deliver results.

1-in-4 teams report difficulty implementing AI tools, and nearly 30% cite integration and workflow inefficiencies as their top frustration, according to our Unmet AI Needs report.

The culprit? A disconnected AI ecosystem. When teams spend more time wrestling with disconnected tools than delivering outcomes, AI leaders risk ballooning costs, stalled ROI, and high talent turnover. 

The hidden costs of fragmented AI tools

AI practitioners spend more time maintaining tools than solving business problems. The biggest blockers? Manual pipelines, tool fragmentation, and connectivity roadblocks.

Imagine if cooking a single dish required using a different stove every single time. Now envision running a restaurant under those conditions. Scaling would be impossible. 

Similarly, AI practitioners are bogged down by the time-consuming, brittle pipelines, leaving less time to advance and deliver AI solutions.

AI integration must accommodate diverse working styles, whether code-first in notebooks, GUI-driven, or a hybrid approach. It must also bridge gaps between teams, such as data science and DevOps, where each group relies on different toolsets. When these workflows remain siloed, collaboration slows, and deployment bottlenecks emerge.

Scalable AI also demands deployment flexibility such as JAR files, scoring code, APIs or embedded applications. Without an infrastructure that streamlines these workflows, AI leaders risk stalled innovation, rising inefficiencies, and unrealized AI potential. 

How integration gaps drain AI budgets and resources 

Interoperability hurdles don’t just slow down teams – they create significant cost implications.

The top workflow restrictions AI practitioners face:

• Manual pipelines. Tedious setup and maintenance pull AI, engineering, DevOps, and IT teams away from innovation and new AI deployments.
  

• Tool and infrastructure fragmentation. Disconnected environments create bottlenecks and inference latency, forcing teams into endless troubleshooting instead of scaling AI.
  
  
• Orchestration complexities.  Manual provisioning of compute resources — configuring servers, DevOps settings, and adjusting as usage scales — is not only time-consuming but nearly impossible to optimize manually. This leads to performance limitations, wasted effort, and underutilized compute, ultimately preventing AI from scaling effectively.
  
  
• Difficult updates. Fragile pipelines and tool silos make integrating new technologies slow, complex, and unreliable. 
  

The long-term cost? Heavy infrastructure management overhead that eats into ROI. 

More budget goes toward the overhead costs of manual patchwork solutions instead of delivering results.

Over time, these process breakdowns lock organizations into outdated infrastructure, frustrate AI teams, and stall business impact.

Why code-first users and developers struggle with AI tools 

Code-first developers prefer customization, but technology misalignment makes it harder to work efficiently.

• 42% of developers say customization improves AI workflows.
  
  
• Only 1-in-3 say their AI tools are easy to use.
  

This disconnect forces teams to choose between flexibility and usability, leading to misalignments that slow AI development and complicate workflows. But these inefficiencies don’t stop with developers. AI integration issues have a much broader impact on the business.

The true cost of integration bottlenecks

Disjointed AI tools and systems don’t just impact budgets; they create ripple effects that impact team stability and operations. 

• The human cost. With an average tenure of just 11 months, data scientists often leave before organizations can fully benefit from their expertise. Frustrating workflows and disconnected tools contribute to high turnover.
  
  
• Lost collaboration opportunities. Only 26% of AI practitioners feel confident relying on their own expertise, making cross-functional collaboration essential for knowledge-sharing and retention.
  

Siloed infrastructure slows AI adoption. Leaders often turn to hyperscalers for cost savings, but these solutions don’t always integrate easily with tools, adding backend friction for AI teams. 

Generative AI and agentic are adding more complexity

With 90% of respondents expecting generative AI and predictive AI to converge, AI teams must balance user needs with technical feasibility.

As King’s Hawaiian CDAO Ray Fager explains:
“Using generative AI in tandem with predictive AI has really helped us build trust. Business users ‘get’ generative AI since they can easily interact with it. When they have a GenAI app that helps them interact with predictive AI, it’s much easier to build a shared understanding.”

With an increasing demand for generative and agentic AI, practitioners face mounting compute, scalability, and operational challenges. Many organizations are layering new generative AI tools on top of their existing technology stack without a clear integration and orchestration strategy. 

The addition of generative and agentic AI, without the foundation to efficiently allocate these complex workloads across all available compute resources, increases operational strain and makes AI even harder to scale.

Four steps to simplify AI infrastructure and cut costs  

Streamlining AI operations doesn’t have to be overwhelming. Here are actionable steps AI leaders can take to optimize operations and empower their teams:

Step 1: Assess tool flexibility and adaptability

Agentic AI requires modular, interoperable tools that support frictionless upgrades and integrations. As requirements evolve, AI workflows should remain flexible, not constrained by vendor lock-in or rigid tools and architectures.

Two important questions to ask are:

• Can AI teams easily connect, manage, and interchange tools such as LLMs, vector databases, or orchestration and security layers without downtime or major reengineering?
  
  
• Do our AI tools scale across various environments (on-prem, cloud, hybrid), or are they locked into specific vendors and rigid infrastructure?
  

Step 2: Leverage a hybrid interface

53% of practitioners prefer a hybrid AI interface that blends the flexibility of coding with the accessibility of GUI-based tools. As one data science lead explained, “GUI is critical for explainability, especially for building trust between technical and non-technical stakeholders.” 

Step 3: Streamline workflows with AI platforms

Consolidating tools into a unified platform reduces manual pipeline stitching, eliminates blockers, and improves scalability. A platform approach also optimizes AI workflow orchestration by leveraging the best available compute resources, minimizing infrastructure overhead while ensuring low-latency, high-performance AI solutions.

Step 4: Foster cross-functional collaboration

When IT, data science, and business teams align early, they can identify workflow barriers before they become implementation roadblocks. Using unified tools and shared systems reduces redundancy, automates processes, and accelerates AI adoption. 

Set the stage for future AI innovation

The Unmet AI Needs survey makes one thing clear: AI leaders must prioritize adaptable, interoperable tools — or risk falling behind. 

Rigid, siloed systems not only slows innovation and delays ROI, it also prevents organizations from responding to fast-moving advancements in AI and enterprise technology. 

With 77% of organizations already experimenting with generative and predictive AI, unresolved integration challenges will only become more costly over time. 

Leaders who address tool sprawl and infrastructure inefficiencies now will lower operational costs, optimize resources, and see stronger long-term AI returns

Get the full DataRobot Unmet AI Needs report to learn how top AI teams are overcoming implementation hurdles and optimizing their AI investments.

The post Why AI leaders can’t afford the cost of fragmented AI tools appeared first on DataRobot.

Learn how to easily and cost-effectively design and procure turnkey automation systems using a robotics platform, taking inspection tasks to the next level.

Researchers have developed an AI-based learning system that recognizes strengths and weaknesses in mathematics by tracking eye movements with a webcam to generate problem-solving hints. This enables teachers to provide significantly more children with individualized support.

I did a podcast this month on how to use technology to increase innovation. Now, I’m not a fan of innovation for its own sake. If you have something that works, innovation can be a bad thing because it may break […]

The post Using AI To Fix The Innovation Problem: The Three Step Solution appeared first on TechSpective.

Visitors to the booth can learn how the company’s entire portfolio of best-in-class robotics and software solutions can help achieve new levels of efficiency in a variety of applications, including food and beverage, general merchandise, apparel and omnichannel warehousing.

The Economic Times reports that the latest upgrade to
ChatGPT — ChatGPT-4.5 — is currently best-in-class.

While many competitors are nearly as good, ChatGPT-4 currently has no equal when it comes to creative writing, handling long-form queries and prompts and engaging in in-depth conversations, according to the Times.

The source of the Economic Times’ report is a popular AI rating service, LMArena.

Volunteers visiting LMArena evaluate AI by testing two unidentified and randomly selected chatbots — and then rating which chatbot responds to their prompt best.

In other news and analysis on AI writing:

*Facebook’s Parent Company Promising ChatGPT Competitor: Facebook inventor Mark Zuckerberg is currently developing a direct competitor to ChatGPT, according to Euro News.

Zuckerberg already has AI software – dubbed Llama – that competes on par with the AI software undergirding ChatGPT.

But so far, Zuckerberg’s AI has only been integrated into various platforms owned by Facebook parent company Meta – and never unveiled as a stand-alone, ChatGPT competitor.

*AI Now Great at Conducting Interviews, Too: Veteran journalist Alex Kantrowitz has discovered a disturbing truth.

Not only can AI write incredibly well: It can also conduct interviews like a news reporter.

Case in point: Kantrowitz says a fellow journalist – Evan Ratliff – recently used voice-powered AI to conduct an interview with a tech CEO.

The result, according to Kantrowitz: “When Ratliff listened to the recording, he was surprised to find the CEO really opened up.

“He was a little more forthcoming with the AI than he was with me,” Ratliff told me.

“There’s a quality of, you don’t necessarily feel like there’s someone there and you might be a little more intimate than you would have otherwise. And that can be very valuable in an interview for a reporting project.”

*ChatGPT-Maker Mulling $20,000/Month Charge for Advanced AI Agents: AI’s Next Big Thing – AI agents that can work autonomously and do things like operate on the Web on your behalf – may be coming with a hefty price tag.

ChatGPT-Maker OpenAI is reportedly weighing a $20,000/month charge for a PhD-level agent designed to do highly advanced research for you.

Meanwhile, AI software developer agents might go for $10,000/month and a knowledge worker agent is being floated at $2,000/month.

Wow — from ‘AI collaborator’ to ‘AI employee.’

That was fast.

*Microsoft Copilot Offers More Freebies: Users of ChatGPT competitor MS Copilot now have two more reasons to stick with the chatbot: Free access to ThinkDeeper and voice.

Like many new deep research tools cropping-up in the market, Copilot ThinkDeeper does a more extensive search and analysis to question as compared to the standard response from Copilot.

Meanwhile, Copilot Voice enables you to operate Copilot with your voice – rather than by using a keyboard.

*Google AI Overviews Gets an Upgrade: Writers who rely on Google AI Overviews for some research should expect better performance.

Specifically, AI Overviews – which study a number of links associated with a search and auto-generate a written summary – are now able to handle tougher questions, according to Robby Stein, VP of product, Google Search.

Plus, AI Overviews is also getting a new, experimental ‘AI Mode.’

Observes Robby Stein, VP of product, Google Search: “This new Search mode expands what AI Overviews can do with more advanced reasoning, thinking and multimodal capabilities so you can get help with even your toughest questions.

“You can ask anything on your mind and get a helpful AI-powered response with the ability to go further with follow-up questions and helpful Web links.”

*Duke University Joins Study on How to Better Embed AI in Education and Government: Duke University – along with 15 other universities – has joined OpenAI’s ‘NextGenAI Consortium’ to analyze how to better integrate AI into education and government.

Observes Brad Lightcap, OpenAI chief operating officer: “A close collaboration with universities is essential to our mission of building AI that benefits everyone.

“NextGenAI will accelerate research progress and catalyze a new generation of institutions equipped to harness the transformative power of AI.”

*ChatGPT Rival Anthropic Snags $3.5 Billion in New Funding: Anthropic – makers of the ChatGPT rival Claude chatbot – has just snagged $3.5 billion in new funding.

Anthropic was founded by former researchers from OpenAI, whose mission is to develop AI with firmer safety guardrails.

Competing in the same space when it comes to AI writing are Google Gemini, X’s Grok 3 – and hundreds of custom-tailored AI writing solutions specially designed for marketing, education, technical writing, law, health and other genres.

*New AI Email Marketing Tool Released: A new AI-powered email marketing platform – Stripo – is promising enhanced automation for email marketers.

Stripo’s AI Assistant – according to Oleksandra Khlystova, PR team lead, Stripo — enhances the email creation process by:

~Generating emails instantly – AI-powered automation reduces time spent on production

~Optimizing email design and structure – AI ensures well-structured layouts while allowing users to fine-tune branding

~Improving content clarity – AI-generated emails maintain strong readability, minimizing the need for manual editing

*AI BIG PICTURE: New Study Finds AI-Powered Writing a Big Hit Among Many White Collar Pros: Stanford University researchers have found that AI writing is being heavily embraced by many white collar workers.

Observes writer Matthias Bastian: “The impact is particularly noticeable in press releases, where up to 24% of content now comes from generative AI systems, or shows significant AI modification.

“The researchers suspect that actual AI adoption rates are higher than their analysis suggests.

“It likely missed heavily human-edited content and text from advanced AI models that closely mimic human writing.

“The study also didn’t examine other potential AI writing use cases, such as social media content creation.”

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–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.

The post New ChatGPT-4.5 Leads the Pack appeared first on Robot Writers AI.

Researchers at the Institute of Technology, University of Tartu, present a robotics concept in which temporary robot embodiments and movement pathways are spun in situ from a polymer solution. They demonstrate an ad hoc gripper for delicate handling and a bridge for crossing debris fields and natural terrain.

Imagine that a robot is helping you clean the dishes. You ask it to grab a soapy bowl out of the sink, but its gripper slightly misses the mark.

Combining RightHand Robotics' innovative robotic piece-picking technology with Rockwell Automation’s industry-leading expertise, the collaboration aims to offer smarter, more efficient, and fully integrated automation solutions for supply chains worldwide.

Claire chatted to Vali Lalioti from the University of the Arts London about how art, culture and robotics interact.

Vali Lalioti is a pioneering designer, computer scientist and innovator. She has a PhD in Computer Science, an MRes in Design and an MBA, and extensive international leadership, research and innovation experience in Silicon Valley, Africa, China, Japan and Europe. Vali is passionate about how technology interacts with society and talks globally on women in tech, art and technology education and her research in societal applications for well-being, healthy ageing and art. She developed the first ever BBC Augmented Reality production in 2003 and has introduced the UK’s first Creative Robotics University Degrees.

Researchers have enabled a man who is paralyzed to control a robotic arm through a device that relays signals from his brain to a computer. He was able to grasp, move and drop objects just by imagining himself performing the actions.

Difference Between Artificial Intelligence and Machine Learning

Nowadays, Artificial Intelligence (AI) and Machine learning (ML) technologies are the two most trending technologies. Many companies are investing in AI and ML applications to transform the existing business processes.
Most of the people are confused about the difference between Artificial Intelligence and Machine Learning. So, we are here to clear your confusion!

Today, in this article, we will be giving a detail about what is AI? What is ML? And what is the major difference between AI and ML technologies.

What is Artificial Intelligence (AI)?

Artificial Intelligence is defined as a smart concept that enables machines to perform various tasks done by humans. AI becomes more popular nowadays with its automation and intelligent features.
AI has been in talks since long back. Gradually, the technology is moving to the next level. The researchers continue to invent something new in AI. Artificial Intelligence machines can solve complex calculations.
AI along with ML techniques, it has been scientifically proven to reflect human decision processes and improve machine intelligence.

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How Does AI Works and Why Is AI So Important?

• AI can automate the every task which is done by human previously
• AI frequently performs high-volume machine tasks efficiently
• Industries are improving their tasks using AI capabilities
• AI-based apps, conversational tools, and chatbots help companies in improving digital marketing
• AI can build fraud detection systems to identify and track illegal access to data systems or network
• AI uses ML to predict the future outcomes
• AI apps in healthcare used to detect diseases with high accuracy
• AI in automobile used for developing autonomous cars

The field of Applied AI is still observing advancements. We can state that advancements in AI is welcoming more innovations in ML. As a subset of AI, machine learning program is giving more valuable insights and predictions into data. Thus, ML is supporting new research works in AI.

What is Machine Learning?

The machine learning is best defined as an important application of AI, which allows a computer or machine to learn from input data and improve the experience without the need for explicit programming. The primary aim of the advanced machine learning algorithms is to allow systems to learn automatically without one’s interaction.

Rapid Growth of Machine Learning

Driven by the advancements in AI, the demand for ML techniques is expanding rapidly. ML allows the software to predict future outcomes accurately.

In addition, a vast amount of digital data over the internet is increasing the demand for ML solutions. In particular, digital businesses are highly adopting ML, and deep learning apps to manage their customers proficiently.
The researchers thought that instead of training machines how to perform, it’s better to code them once to do repeated tasks automatically. This trend increased the demand for the development of machine learning, deep learning, data analysis, and predictive analytics.

How Does Machine Learning Works?

How does AI differ from machine learning:

• Step 1: Learns from a trained data set
• Step 2: Identifies dissimilar data from a group of similar data and hence measures error rate
• Step 3: Identifies noise attributes to improve the processing capacity
• Step 4: Data validation and testing processes to deliver accurate error measure
• Step 5: Insights into data

Difference Between AI and Machine Learning: Artificial Intelligence Vs Machine Learning

Here are the top differences between AI and ML:

The above table helps you learn how does AI differ from machine learning. Being a subset of AI, the difference between machine learning and AI is specific to learning and insights extraction.

What is Generative AI?

Generative AI is a form of artificial intelligence that produces original content, such as images, text, or music, based on learning from current data. It utilizes models such as GANs and transformers to create realistic results that mimic actual instances. The technology applies to industries such as art, entertainment, and medicine.

What Is The Difference Between Generative AI And Machine Learning

Both generative AI (GenAI) and machine learning fall under artificial intelligence but have varying uses. Machine learning aims at model training for the purpose of recognizing patterns within data and prediction or decision-making, including categorization of data, predicting trends, or the identification of outliers. Machine learning incorporates methodologies such as supervised, unsupervised, and reinforcement learning.

Generative AI, in contrast, is a niche field of machine learning whose purpose is to generate new content, images, text, or music from given prompts. The major difference between generative AI and Machine Learning is mostly about analysis and prediction.

Another difference between Gen AI and machine learning is in their model training goals. Machine learning models are trained to achieve optimal performance on tasks such as prediction by maximizing accuracy. In contrast, generative AI models are trained so that they will discover the structural and distribution information in the data to produce fresh, related data.

Is ChatGPT AI or Machine Learning

ChatGPT is powered by AI technology that uses machine learning and deep learning to better understand user prompts and create human-like responses. It’s trained on massive amounts of text data to learn about language patterns, context, and structure, enabling it to respond to questions, engage in conversation, and help with other tasks. Although ChatGPT itself uses machine learning, it is a subcategory of the larger AI genre because it demonstrates intelligent behavior such as natural language generation and understanding.

Neural Networks

The primary reason for the development of Neural Networks is to train the systems to replicate exactly like humans.

A Neural Network system can categorize the data in a manner as human brain do. These systems can recognize images and categorize them based on the elements they comprise. In the image below, the nervous system takes an input image, processes it, and finally identifies objects using previously gained experiences.

Based on the trained data, it can make decisions, predictions, and statements with confidence. Along with the feedback loop, it can decide the predicted decisions are wrong or right. Thus, neural network systems can modify the approach it takes in the future.

Accordingly ML apps can read and understand the input text and categorize whether that text is a complaint or greetings. In addition, ML applications can also listen to music and determines whether it makes a person happy or sad.

All these are a few applications of ML and neural network systems. The major idea behind all research works is connecting digital data and electronic devices intelligently. To reach this, AI also uses natural language processing (NLP) to efficiently understand human language.
NLP is highly dependent on ML techniques. The NLP-based apps can interpret written/spoken language and respond to the user in the same way.

Machine Learning Vs Neural Networks

Machine Learning	Neural Networks
Falls under the field of artificial intelligence	A sub-field of machine learning
Enables machines to automatically learn and process input data without being explicitly programmed.	Also called as artificial neural network used for categorizing data/images as our bran do
Types: Supervised and unsupervised learning methods	Types: Convolutional neural networks and recurrent neural networks
Mostly used in healthcare, retail, e-commerce, pricing strategies, customer retention etc.	Applied in finance, healthcare, retail, stock prediction, and etc.
Google Maps, Siri, and google search are the best examples of machine learning.	Image recognition, compression, and search engines are the best examples of neural networks.

Advanced Artificial Intelligence and Machine Learning Market Overview:

Increased investment in AI technologies, the growing need to process large amounts of data and the lack of experienced technicians to manage business tasks are key growth factors of the artificial intelligence market size. In between 2016-2025, the market size is expected to project $169.41 billion by 2025 from $4.06 billion in 2016.

To Conclude, Next-Level of AI and ML Offers Huge Opportunities to Businesses

Despite the difference between AI and ML technologies is being thin, we can understand that the combination of AI and machine learning models provides intelligent business processes. Different industries ranging from healthcare and banking to manufacturing and e-commerce are widening business opportunities. Thus, AI, ML, deep learning, and neural networks expand your brand awareness.

For instance, the sales and marketing teams are using ML systems to detect the behavior of its customers search. Thus, AI & ML apps for marketing and sales industry are providing growth benefits to them. Multiple developments in AI leads to the development of ML technology even more.

Connect with USM to know more the benefits of AI and ML Technologies.
Hope, this article makes you understand the basic difference between AI and ML. We would like to add more valuable information related to Artificial intelligence, reinforcement learning, computer science, data science, big data, and deep learning technologies.

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