Google, on Wednesday, released the Model Card Toolkit (MCT) to bring explainability in machine learning models. The information provided by the library will assist developers in making informed decisions while evaluating models for its effectiveness and bias.
MCT provides a structured framework for reporting on ML models, usage, and ethics-informed evaluation. It gives a detailed overview of models’ uses and shortcomings that can benefit developers, users, and regulators.
To demonstrate the use of MCT, Google has also released a Colab tutorial that has leveraged a simple classification model trained on the UCI Census Income dataset.
You can use the information stored in ML Metadata (MLMD) for explainability with JSON schema that is automatically populated with class distributions and model performance statistics. “We also provide a ModelCard data API to represent an instance of the JSON schema and visualize it as a Model Card,” note the author of the blog. You can further customize the report by selecting and displaying the metrics, graphs, and performance deviations of models in Model Card.
The detailed reports such as limitations, trade-offs, and other information from Google’s MCT can enhance explainability for users and developers. Currently, there is only one template for representing the critical information about explainable AI, but you can create numerous templates in HTML according to your requirement.
Anyone using TensorFlow Extended (TFX) can avail of this open-source library to get started with explainable machine learning. For users who do not utilize TFX, they can leverage through JSON schema and custom HTML templates.
Over the years, explainable AI has become one of the most discussed topics in technology as today, artificial intelligence has penetrated in various aspects of our lives. Explainability is essential for organizations to bring trust in AI models among stakeholders. Notably, in finance and healthcare, the importance of explainability is immense as any deviation in the prediction can afflict users. Google’s MCT can be a game-changer in the way it simplifies the model explainability for all.
Intel’s stocks plunged around 18% as the company announced that it is considering outsourcing the production of chips due to delays in the manufacturing processes. This wiped out $42 billion from the company as the stocks were trading at a low of $49.50 on Friday. Intel’s misery with production is not new. Its 10-nanometer chips were supposed to be delivered in 2017, but Intel failed to produce in high-volumes. However, now the company has ramped up the production for its one of the best and popular 10-nanometer chips.
Intel’s Misery In Chips Manufacturing
Everyone was expecting Intel’s 7-nanometer chips as its competitor — AMD — is already offering processors of the same dimension. But, as per the announcement by the CEO of Intel, Bob Swan, the manufacturing of the chip would be delayed by another year.
While warning about the delay of the production, Swan said that the company would be ready to outsource the manufacturing of chips rather than wait to fix the production problems.
“To the extent that we need to use somebody else’s process technology and we call those contingency plans, we will be prepared to do that. That gives us much more optionality and flexibility. So in the event there is a process slip, we can try something rather than make it all ourselves,” said Swan.
This caused tremors among shareholders as it is highly unusual for a 50 plus year world’s largest semiconductor company. In-house manufacturing has provided Intel an edge over its competitors as AMD’s 7nm processors are manufactured by Taiwan Semiconductor Manufacturing Company (TSMC). If Intel outsources the manufacturing, it is highly likely that TSMC would be given the contract, since they are among the best in producing chips.
But, it would not be straight forward to tap TSMC as long-term competitors such as AMD, Apple, MediaTek, NVIDIA, and Qualcomm would oppose the deal. And TSMC will be well aware that Intel would end the deal once it fixes its problems, which are currently causing the delay. Irrespective of the complexities in the potential deal between TSMC and Intel, the world’s largest chipmaker — TSMC — stock rallied 10% to an all-time high as it grew by $33.8 billion.
Intel is head and shoulder above all chip providers in terms of market share in almost all categories. For instance, it has a penetration of 64.9% in the market in x86 computer processors or CPUs (2020), and Xeon has a 96.10% market share in server chips (2019). Consequently, Intel’s misery gives a considerable advantage to its competitors. Over the years, Intel has lost its market penetration to AMD year-over-year (2018 – 2019): Intel lost 0.90% in x86 chips, -2% in server, -4.50% in mobile, and -4.20% in desktop processors. Besides, NVIDIA eclipsed Intel for the first time earlier this month by becoming the most valuable chipmaker.
Undoubtedly, Intel is facing the heat from its competitors, as it is having a difficult time maneuvering in the competitive chip market. But, the company is striving to make necessary changes in order to clean up its act.
On Monday, Intel’s CEO announced changes to the company’s technology organization and executive team to enhance process execution. As mentioned earlier, the delay did not go well with the company, which has led to the revamp in the leadership, including the ouster of Murthy Renduchintala, Intel’s hardware chief, who will be leaving on 3 August.
Intel poached Renduchintala from Qualcomm in February 2016. He was given a more prominent role in managing the Technology Systems Architecture and Client Group (TSCG).
The press release noted that TSCG will be separated into five teams, whose leaders will report directly to the CEO.
List of the teams:
Technology Development will be led by Dr. Ann Kelleher, who will also lead the development of 7nm and 5nm processors
Manufacturing and Operations, which will be monitored by Keyvan Esfarjani, who will oversee the global manufacturing operations, product ramp, and the build-out of new fab capacity
Design Engineering will be led by an interim leader, Josh Walden, who will supervise design-related initiatives, along with his earlier role of leading Intel Product Assurance and Security Group (IPAS)
Architecture, Software, and Graphics will be continued to be led by Raja Koduri. He will focus on architectures, software strategy, and dedicated graphics product portfolio
Supply Chain will be continued to be led by Dr. Randhir Thakur, who will be responsible for the importance of efficient supply chain as well as relationships with key players in the ecosystem
Intel, with this, had made a significant change in the company to ensure compliance with the timeline it sets. Besides, Intel will have to innovate and deliver on 7nm before AMD creates a monopoly in the market with its microarchitectures that are powering Ryzen for mainstream desktop and Threadripper for high-end desktop systems.
Although the chipmaker revamped the leadership, Intel’s misery might not end soon; unlike software initiatives, veering in a different direction and innovating in the hardware business takes more time. Therefore, Intel will have a challenging year ahead.
Artificial intelligence is one of the most talked-about topics in the tech landscape due to its potential for revolutionizing the world. Many thought leaders of the domain have spoken their minds on artificial intelligence on various occasions in different parts of the world. Today, we will list down the top artificial intelligence quotes that have an in-depth meaning and are/were ahead of time.
Here is the list of top quotes about artificial intelligence: –
Artificial Intelligence Quote By Jensen Huang
“20 years ago, all of this [AI] was science fiction. 10 years ago, it was a dream. Today, we are living it.”
JENSEN HUANG, CO-FOUNDER AND CEO OF NVIDIA
The quote on artificial intelligence by Jensen Huang was said during NVIDIA GTC 2021 while announcing several products and services during the event. Over the years, NVIDIA has become a key player in the data science industry that is assisting researchers in further the development of the technology.
Quote On Artificial Intelligence By Stephen Hawking
“Success in creating effective AI, could be the biggest event in the history of our civilization. Or the worst. We just don’t know. So we cannot know if we will be infinitely helped by AI, or ignored by it and side-lined, or conceivably destroyed by it. Unless we learn how to prepare for, and avoid, the potential risks, AI could be the worst event in the history of our civilization. It brings dangers, like powerful autonomous weapons, or new ways for the few to oppress the many. It could bring great disruption to our economy.”
Stephen Hawking, 2017
Stephen Hawking’s quotes on artificial intelligence are very optimistic. Some of the famous quotes on artificial intelligence came from Hawking in 2014 when the BBC interviewed him. He said artificial intelligence could spell the end of the human race.
I have been banging this AI drum for a decade. We should be concerned about where AI is going. The people I see being the most wrong about AI are the ones who are very smart, because they can not imagine that a computer could be way smarter than them. That’s the flaw in their logic. They are just way dumber than they think they are.
Elon Musk, 2020
Musk has been very vocal about artificial intelligence’s capabilities in changing the way we do our day-to-day tasks. Earlier, he had stressed on the fact that AI can be the cause for world war three. In his Tweet, Musk mentioned ‘it [war] begins’ while quoting a news, which noted Vladimir Putin, President of Russia, though on the ruler of the world; the president said the nation that leads in AI would be the ruler of the world.
Unlike negative quotes on artificial intelligence by others, Zuckerberg does not believe artificial intelligence will be a threat to the world. In his Facebook live, Zuckerberg answered a user who asked about people like Elon Musk’s opinion about artificial intelligence. Here’s what he said:
“I have pretty strong opinions on this. I am optimistic. I think you can build things and the world gets better. But with AI especially, I am really optimistic. And I think people who are naysayers and try to drum up these doomsday scenarios. I just don’t understand it. It’s really negative and in some ways, I actually think it is pretty irresponsible.”
Mark Zuckerberg, 2017
Larry Page’s Quote
“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.”
Larry Page
Stepped down as the CEO of Alphabet in late 2019, Larry Page has been passionate about integrating artificial intelligence in Google products. This was evident when the search giant announced that the firm is moving from ‘Mobile-first’ to ‘AI-first’.
Sebastian Thrun’s Quote On Artificial Intelligence
“Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It’s really an attempt to understand human intelligence and human cognition.”
Sebastian Thrun
Sebastian Thrun is the co-founder of Udacity and earlier established Google X — the team behind Google self-driving car and Google Glass. He is one of the pioneers of the self-driving technology; Thrun, along with his team, won the Pentagon’s 2005 contest for self-driving vehicles, which was a massive leap in the autonomous vehicle landscape.
Artificial Intelligence is powering the next generation of self-driving cars and bikes all around the world by manoeuvring automatically without human intervention. To stay ahead of this trend, companies are extensively burning cash in research and development for improving the efficiency of the vehicles.
More recently, Hyundai Motor Group said that it has devised a plan to invest $35 billion in auto technologies by 2025. With this, the company plans to take lead in connected and electrical autonomous vehicles. Hyundai also envisions that by 2030, self-driving cars will account for half of the new cars and the firm will have a sizeable share in it.
Ushering in the age of driverless cars, different companies are associating with one another to place AI at the wheels and gain a competitive advantage. Over the years, the success in deploying AI in autonomous cars has laid the foundation to implement the same in e-bikes. Consequently, the use of AI in vehicles is widening its ambit.
Utilising AI, organisations are not only able to autopilot on roads but also navigate vehicles to parking lots and more. So how exactly does it work?
Artificial Intelligence Behind The Wheel
In order to drive the vehicle autonomously, developers train reinforcement learning (RI) models with historical data by simulating various environments. Based on the environment, the vehicle takes action, which is then rewarded through scalar values. The reward is determined by the definition of the reward function.
The goal of RI is to maximise the sum of rewards that are provided based on the action taken and the subsequent state of the vehicle. Learning the actions that deliver the most points enables it to learn the best path for a particular environment.
Over the course of training, it continues to learn actions that maximise the reward, thereby, making desired actions automatically.
The RI model’s hyperparameters are amended and trained to find the right balance for learning ideal action in a given environment.
The action of the vehicle is determined by the neural network, which is then evaluated by a value function. So, when an image through the camera is fed to the model, the policy network also known as actor-network decides the action to be taken by the vehicle. Further, the value network also called as critic network estimates the result given the image as an input.
The value function can be optimized through different algorithms such as proximal policy optimization, trust region policy optimization, and more.
What Happens In Real-Time?
The vehicles are equipped with cameras and sensors to capture the scenario of the environment and parameters such as temperature, pressure, and others. While the vehicle is on the road, it captures video of the environment, which is used by the model to decide the action based on its training.
Besides, a specific range is defined in the action space for speed, steering, and more, to drive the vehicle based on the command.
Other Advantages Of Artificial Intelligence In Vehicles Explained
While AI is deployed for auto-piloting vehicles, more notably, AI in bikes are able to assist people in increasing security. Of late, in bikes, AI is learning to understand the usual route of the user and alerts them if the bike is moving in a suspicious direction, or in case of unexpected motion. Besides, in e-bike, AI can analyse the distance to the destination of cyclist and enhance the power delivery for minimizing the time to reach the endpoint.
Outlook
The self-driving vehicles have great potential to revolutionize the way people use vehicles by rescuing them from doing repetitive and tedious driving activities. Some organisations are already pioneering by running shuttle services through autonomous vehicles. However, governments of various countries do not permit firms to run these vehicles on a public road by enacting legislations. Governments are critical about the full-fledged deployment of these vehicles.
We are still far away from democratizing self-driving cars and improve our lives. But, with the advancement in artificial intelligence, we can expect that it will clear the clouds and steer their way on roads.
In healthcare, every update to a provider’s credentials, license, or location has to be accurate across multiple systems, or the entire chain of care and reimbursement can be disrupted. Hospitals, insurers, and administrators know that a single outdated record can trigger delays, penalties, or even patient safety risks. As data continues to multiply, managing it with spreadsheets and manual oversight is no longer realistic. Artificial intelligence is stepping in to clean, connect, and maintain this data in real time. The result isn’t just better efficiency, it’s a stronger, more compliant healthcare ecosystem. Here’s how AI-powered provider data management is quietly reshaping one of medicine’s most overlooked foundations.
Why Provider Data Management Solutions Matter
The healthcare industry runs on information, but much of that information is fragmented. Each hospital, insurer, and credentialing body maintains its own databases filled with overlapping or outdated provider details. When those systems don’t align, the consequences can ripple through scheduling, billing, and patient care. That’s where provider data management solutions come in to create a single, verified source of truth for provider records across the entire healthcare network. These systems streamline data collection, validation, and updates while minimizing human error.
Imagine the complexity of managing thousands of physicians, each with changing certifications, specialties, and affiliations. Inconsistent data might lead to insurance claims being denied, compliance violations, or incorrect directory listings that frustrate patients. Provider data management tools fix this by using intelligent matching algorithms and automated workflows to ensure that the information across all platforms stays synchronized.
The Real Meaning of Using AI In Healthcare
Artificial intelligence has become a big buzzword across industries, but its impact in healthcare carries a unique weight. Using AI in healthcare involves more than automating processes; it’s about ensuring that the technology operates with transparency, safety, and regulatory alignment. In data management, that means creating systems that not only process information faster but also maintain the privacy and integrity required by healthcare law.
AI helps healthcare organizations detect inconsistencies in provider data at a scale no human team could handle. It identifies mismatches in credentials, predicts when licenses are due for renewal, and flags records that may violate compliance rules. Machine learning algorithms continuously learn from new information, which means the more data they process, the more accurate they become.
AI doesn’t replace compliance officers or administrators; instead, it supports them by turning mountains of raw data into actionable insights. When humans and machines collaborate, the result is a smarter, more proactive compliance system that anticipates problems instead of reacting to them.
Automating Compliance With Machine Learning
Regulatory compliance in healthcare is one of the most demanding operational challenges. Every state, insurer, and federal agency has its own standards for provider credentialing and reporting. Missing even a minor update can result in fines or lost revenue. Machine learning now offers a way to simplify that burden.
By scanning vast datasets across multiple sources, AI systems can detect patterns that point to compliance risks before they escalate. For instance, if a physician’s license expires soon or if their credentials don’t match what’s listed in a payer directory, the system can alert administrators instantly. This level of automation cuts down on manual auditing and gives compliance teams the time to focus on higher-level strategy.
Integrating Systems for a Unified Healthcare Network
One of the biggest barriers to effective provider data management is fragmentation. Hospitals, insurers, and clinics all use different platforms to store their information. That isolation creates gaps, redundancies, and compliance blind spots. Integration solves that.
AI-driven integration tools now make it possible to connect disparate systems through APIs and data harmonization frameworks. Once connected, they ensure that any change, whether it’s a provider’s address, specialty, or credential, is automatically updated everywhere it needs to be. This synchronization is critical in an industry where accuracy is required.
Beyond efficiency, integration improves collaboration. When every department works from the same verified data, it reduces confusion and speeds up decisions. Physicians can be onboarded faster, payers can process claims more accurately, and patients can find the right care without running into outdated directories.
Balancing Automation With Expertise
For all its advantages, AI can’t replace human judgment. In fact, its success depends on the people who guide and interpret its outputs. Compliance teams, data managers, and healthcare administrators provide the ethical and contextual framework that keeps technology aligned with real-world needs.
Automation handles the repetitive, data-heavy work, like checking credentials or identifying expired licenses. But humans are still essential for complex decision-making. When regulations change or exceptions arise, it takes experience and critical thinking to apply the right interpretation. The best provider data management strategies blend automation and expertise into a single workflow, ensuring accuracy without sacrificing accountability.
This balance also keeps organizations adaptive. Regulations shift, new technologies emerge, and healthcare priorities evolve. Teams that embrace AI as a partner rather than a replacement tend to innovate faster and maintain higher compliance standards. The future of healthcare data isn’t fully automated; it’s intelligently collaborative.
Artificial intelligence is reshaping how pharmaceuticals are produced—not through sweeping, headline-grabbing changes, but through steady, behind-the-scenes improvements. In a field where even the smallest variation can affect safety and compliance, AI is becoming an essential tool for manufacturers aiming to meet today’s complex demands.
Pharmaceutical production involves countless variables: fluctuating raw material quality, environmental factors, tight regulatory requirements, and the ever-present risk of human error. AI doesn’t eliminate these challenges—it helps manage them with more precision and consistency than ever before.
Adaptive Systems That Learn and Improve
Unlike traditional automation, AI systems evolve over time. Through continuous data intake and analysis, machine learning tools adjust processes automatically, becoming smarter and more efficient as they go. This is especially useful for optimizing production performance and minimizing equipment failure.
When equipment begins to wear down, AI can pick up on early warning signs and recommend preventive maintenance. When production parameters begin to shift, AI can make real-time adjustments. The result is less waste, fewer delays, and stronger quality control across every batch.
Outside the production floor, AI is also driving smarter logistics. From forecasting demand to identifying supply chain disruptions before they happen, AI tools give manufacturers the flexibility and foresight needed to stay ahead in an unpredictable global market.
Supporting Compliance Without Slowing Progress
In an industry where regulations are non-negotiable, modernization can sometimes feel like a risk. AI helps ease that friction. Tools powered by natural language processing can rapidly review complex regulatory texts, helping teams understand compliance requirements more efficiently. Automated tracking systems ensure transparency and traceability from formulation to final shipment.
These technologies don’t just reduce errors—they also help manufacturers move forward confidently, knowing their innovations are supported by systems built for accountability.
Looking Ahead: Smarter Systems for Safer Products
AI isn’t just a productivity booster—it’s becoming the foundation for safer, more resilient pharmaceutical operations. As the technology matures, it’s offering manufacturers more than just a competitive edge. It’s helping create systems that are more responsive, more accurate, and better equipped to meet the needs of modern medicine.
The impact may be subtle, but it’s significant. With AI integrated across their operations, pharmaceutical companies are laying the groundwork for a future built on precision, reliability, and smarter decision-making at every step. For additional insight into how AI is redefining standards in pharmaceutical production, explore the visual guide accompanying this article from Advanced Technology Services, provider of MRO asset management.
The browser wars have entered a new chapter, and this time, artificial intelligence is the battlefield. The AI browser war is intensifying as tech giants and startups vie to shape the future of web navigation. OpenAI’s ChatGPT Atlas, launched in October 2025, represents a bold reimagining of what web browsing can be—with AI woven directly into every interaction. Meanwhile, Perplexity’s Comet has carved its own niche as a research-first, context-aware browsing companion. Yet perhaps the most striking story isn’t what these upstarts are doing right, but what Google Chrome—the browser that commands over 66% market share—has been doing wrong: moving painfully slow in an era that demands speed.
OpenAI Atlas: The Browser Built for AI-First Workflows
Atlas isn’t just Chrome with ChatGPT bolted on. It’s a fundamental rethinking of browser architecture where AI understands your context, remembers your preferences, and acts on your behalf. The standout feature is “agent mode,” which can execute multi-step tasks autonomously—imagine asking it to gather ingredients from a recipe, add them to a shopping cart, and place an order, all while you focus on other work.
Privacy controls are robust: browser memories are optional, users can toggle ChatGPT’s visibility on specific sites, and the system won’t use your browsing data for training unless you explicitly opt in. Atlas also implements critical safety guardrails—it can’t run code, download files, or access your file system, and it pauses before taking actions on financial sites. These thoughtful boundaries address legitimate concerns about AI agents operating with logged-in credentials.
Comet by Perplexity: The Researcher’s Secret Weapon
Comet takes a different approach, prioritizing cross-tab intelligence and citation-backed answers over full autonomy. It excels at synthesizing information from multiple sources, comparing products across tabs, and providing verifiable references for every claim. Where Atlas emphasizes automation, Comet emphasizes understanding—it’s the browser for users who want AI as a research partner, not a replacement.
Built on Chromium, Comet maintains compatibility with Chrome extensions while offering native conversational search and task-driven workflows. Its privacy model includes local storage options and stricter data controls, positioning it as the choice for users skeptical of sending every browsing action to cloud servers.
The Critical Difference: Speed vs. Control
Atlas wins on automation and depth of integration for users already invested in the ChatGPT ecosystem. Agent mode’s ability to handle end-to-end workflows is genuinely transformative, though it remains in preview with acknowledged limitations around complex tasks. Comet wins on transparency and research workflows, with its citation-first approach building trust through verifiability.
Here’s the uncomfortable truth: Google invented the modern AI era with Transformer architecture and has world-class models in Gemini—yet Chrome feels like it’s playing catch-up in its own game. Google announced its “biggest upgrade in Chrome’s history” in September 2025, integrating Gemini directly into the browser. But the rollout has been frustratingly incremental.
Gemini in Chrome can summarize pages and answer questions about open tabs—features that sound impressive until you realize Atlas and Comet launched with these capabilities baked in from day one. Google promises “agentic capabilities” that will handle multi-step tasks, but those features remain largely aspirational, described as “upcoming” and “future updates.” Meanwhile, Atlas shipped with working agent mode at launch.
The delay is particularly puzzling given Google’s resources and Chrome’s dominant position. The company should have been first to market with an AI-native browser, not scrambling to match upstart competitors. Whether it’s organizational inertia, regulatory concerns over antitrust issues, or simply underestimating how quickly the market would move, Chrome’s sluggish AI integration represents a strategic misstep.
The Verdict
The AI browser war has a clear winner. For users wanting cutting-edge AI automation with strong privacy safeguards, Atlas delivers today what Chrome promises for tomorrow. For researchers demanding source transparency and cross-tab intelligence, Comet excels. For those hoping Google Chrome would lead this revolution—prepare for disappointment. In the race to define AI-powered browsing, the incumbent waited too long, and challengers are sprinting past.
As digital tools reshape how financial institutions interact with their customers, they also present new security challenges. Online platforms must do more than deliver convenience—they need to defend against increasingly advanced cyber threats. With fraudsters targeting vulnerable systems and blending in with legitimate users, a stronger, more flexible approach to protection is critical.
The limitations of legacy defenses are becoming clear. Attackers now rely less on brute-force hacks and more on techniques like phishing or credential stuffing. Once they gain access, their behavior can resemble that of a typical customer, making threats harder to detect. Security systems based solely on fixed rules often fail to catch these subtleties, especially when activity spans across channels like web, mobile, and customer support.
To meet this challenge, modern platforms are turning to adaptive security. These systems analyze user behavior, device data, and transaction flow in real time to flag anomalies. Instead of waiting for an alert or relying on static rules, they adjust automatically to emerging threats. This intelligence-driven model is supported by a strong human layer as well—well-trained staff and informed customers are key to spotting warning signs early and acting quickly.
Designing platforms with security in mind from the start also makes a difference. By addressing risks during development and building protections into the user experience, financial institutions can reduce vulnerabilities without disrupting service. Smart integration of security ensures smoother interactions while keeping sensitive data safe.
Artificial intelligence plays a growing role in this process. By learning from each transaction, AI tools improve at spotting unusual activity, reducing false positives, and helping teams resolve issues faster. The result is improved efficiency, better compliance, and a stronger defense without the added strain on fraud departments.
At its core, investing in cybersecurity is an investment in customer trust. When users see that a platform takes their protection seriously, they’re more likely to stay loyal, recommend the service, and deepen their relationship. In today’s competitive market, trust and security are not separate goals—they work hand in hand. Discover practical ways to strengthen digital platform defenses while enhancing the customer experience in the accompanying resource from Q2 Software, a provider of commercial banking solutions.
GitHub CEO Thomas Dohmke will step down by the end of 2025 to launch a new startup, marking a return to his entrepreneurial roots. In a company-wide blog post, Dohmke explained, “I’ve decided to leave GitHub to become a founder again,” citing his startup origins as the catalyst for his decision. His departure comes as GitHub is in a phase of peak strength—now serving over 150 million developers across more than one billion repositories and forks.
During his tenure, Dohmke oversaw GitHub’s expansion into AI-powered developer tools. This includes major milestones like FedRAMP certification, global growth, and a doubling of AI-driven projects on the platform. His own reflection likened GitHub Copilot’s impact on software development to the revolutionary advent of the personal computer.
Microsoft will not appoint a new CEO. Instead, it will fully integrate GitHub’s leadership under its CoreAI team, spearheaded by Jay Parikh. GitHub will now align more closely with Microsoft’s broader AI and developer tool strategy. Dohmke affirmed he will stay on through the end of the year to guide the transition.
OpenAI’s GPT-5, while impressive, is emphatically not AGI (Artificial General Intelligence). OpenAI CEO Sam Altman himself has cautioned against overhyping its capabilities. During a recent discussion, he admitted the model’s power made him feel “useless,” saying, “I felt useless compared to the AI in this thing that I felt I should have been able to do, and I could not, and it was really hard.” Yet he also reaffirmed that GPT-5 lacks fundamental AGI traits like real-time autonomous learning.
Despite marketing unleashing expectations that GPT-5 signals the dawn of AGI, analysts and critics are urging restraint. Grace Huckins of MIT Technology Review calls GPT-5 “a refined product” that “falls far short of the transformative AI future that Altman has spent much of the past year hyping.”
Expert Skepticism and Highlighted Flaws
Gary Marcus—a persistent and respected critic—was equally measured. On X, he stated that after “nearly three years and billions of dollars, GPT-5 made ‘good progress on many fronts’ but is ‘still part of the pack, not a giant leap forward.’ He concluded it is ‘obviously not AGI.'”
Additionally, emergent research continues to show that LLMs falter even on simple tasks. A recent Apple study demonstrated that even leading LLMs stumble on classic logic puzzles such as the Tower of Hanoi—the kind solvable by children—revealing that more scaling does not equal more reasoning.
Hype vs. Historical Precedents
The current excitement around GPT-5 mirrors past technology hype—like the early-2020s promise that self-driving cars were just around the corner. Despite massive investment and innovation, full autonomy remains elusive a decade later. People today still glimpse the potential in pilot programs and advanced features, but widespread adoption of fully autonomous vehicles remains distant.
Similarly, while LLMs like GPT-5 offer impressive pattern recognition, their underlying architecture—transformers—is fundamentally flawed for achieving AGI. These models rely on statistical pattern-matching, not understanding. As researchers like John Mark Bishop and Judea Pearl—and echoed by Marcus—have argued, deep learning remains “just curve fitting,” lacking causal reasoning and generalizable logic.
The Technology Wall Ahead
Transformers, despite their scalability, hit diminishing returns. Larger models do not necessarily understand context or reasoning better—they just get better at predicting the next word. The Tower of Hanoi results, combined with Marcus’s analysis, suggests we’ve hit a “deep learning wall.”
Thus, even after another decade of incremental improvements, substantial progress toward AGI is unlikely unless new paradigms—like neurosymbolic, causal, or hybrid systems—emerge.
Final Word
GPT-5 is undeniably a leap forward in usability and fluency. But it remains a refined LLM—a tool, not a self-aware intellect. Altman’s “useless” remark underscores this uneasy dissonance between progress and promise. Vocal critics like Gary Marcus remind us: we are still far from true AGI.
Much like the still-distant era of autonomous vehicles, the vision of AGI remains one for the future—perhaps generations away. For now, we should value LLMs for their immediate utility but temper expectations and invest in robust, causal, and verifiable AI foundations.
Google’s Gemini Deep Think mode is now live in the Gemini app for Google AI Ultra subscribers, marking a major step forward in Google’s Deep Think reasoning capabilities. Offered via Gemini 2.5 Pro Deep Think, this multi-agent model intelligently explores multiple reasoning paths before settling on the most accurate solution.
Launched initially at Google I/O 2025 and publicly rolled out in late July, Google’s Gemini Deep Think mode introduces a shift from fast, shallow answers to deliberate, context-aware analysis for complex tasks.
For researchers, scientists, and academics tackling hard problems: Gemini 2.5 Deep Think is here. 🤯
It doesn't just answer, it brainstorms using parallel thinking and reinforcement learning techniques. We put it into the hands of mathematicians who explored what it can do ↓ pic.twitter.com/lsBq3ICvuM
With Deep Think activated, Gemini can outperform across challenging benchmarks—including LiveCodeBench, the International Mathematical Olympiad standard, MMMU multimodal reasoning tests, and advanced coding puzzles. Recent scores include 87.6% on LiveCodeBench and Bronze-level performance on IMO benchmarks, demonstrating real gains over earlier Gemini versions.
Users accessing Gemini’s Deep Think reasoning mode in the app receive more thoughtful, nuanced responses. Gemini can handle layered prompts requiring hypothesis testing, code execution, and evidence-based conclusions—all powered by a parallel thinking engine built on reinforcement learning and large‑context architecture (1M token window).
This premium feature is available now to Google AI Ultra subscribers ($249.99/month in the U.S.). Rolling out across web and mobile, subscribers can access the mode via a new button in the prompt bar when using Gemini 2.5 Pro Deep Think. Wider access via API, enterprise tools, and Workspace integration is planned in the coming months for trusted testers.
Google’s Deep Think reasoning engine transforms Gemini from a conversational assistant into a deep analytical partner. For developers, researchers, content creators, and educators, Deep Think automates complex reasoning tasks—from code design to research synthesis—saving substantive time and mental effort.
By pushing multiple hypotheses and iterating solutions internally before responding, Google’s Deep Think reasoning mode enables more precise and innovative output. This means fewer superficial answers and more actionable, research-grade insights—ideal for generating technical explainers, strategic reports, or educational content.Moreover, Deep Think’s integration into Gemini makes advanced reasoning tools accessible within one unified interface—without switching platforms. As the model continues to evolve with safety layers and benchmarking feedback, Google demonstrates ambition to lead in thoughtful AI, not just fast AI.
Meta’s personal superintelligence vision, announced by CEO Mark Zuckerberg on July 30, 2025, outlines a future where individualized AI assistants evolve through self-improvement to empower personal agency. Zuckerberg argues that recent glimmers of AI systems enhancing themselves indicate that developing meta’s personal superintelligence is no longer theoretical but within sight.
Unlike centralized models aimed at automating work en masse, Zuckerberg envisions a different path. He contends that meta’s personal superintelligence should help people pursue their own goals—enabling creativity, learning, connection, and personal growth, rather than subsidizing humanity through mass automation.
Meta has formally launched its Superintelligence Labs, assembling elite talent from OpenAI, DeepMind, and Anthropic under the leadership of former Scale AI CEO Alexandr Wang and ex‑GitHub CEO Nat Friedman. These labs aim to build high-capacity compute infrastructure—most notably the Hyperion data center and Prometheus cluster with gigawatt-scale power—to support the ambition of delivering personal superintelligence to everyone.
Zuckerberg also emphasized hardware convergence: AI glasses will likely become the dominant form factor for these assistants. These smart, context-aware devices that can see, hear, and interact continuously are seen as essential to personalized AI augmentation.
Together, this strategy positions Meta at the intersection of infrastructure, software, and hardware integration, aiming to scale personal superintelligence broadly across its user base. Yet, Meta also acknowledged the need for robust safety frameworks, citing new risks inherent in superintelligent systems.
Meta’s push for personal superintelligence may fundamentally reshape how individuals create, work, and engage with content. AI assistants deeply tuned to individual goals could streamline idea generation, topic research, and content scripting—ideal tools for explainer video creators, educators, and storytellers. With AI-powered glasses and continuous context awareness, creators could capture real-time insights, transform them into polished visuals or videos, and enhance collaboration with AI collaborators.
By integrating this deeply personal AI into everyday life—and making it accessible across social, productivity, and creative verticals—Meta has the potential to democratize high-level creativity and knowledge synthesis at scale. As Zuckerberg noted, this is not just another automation tool but a new era of personal empowerment.
Google NotebookLM Video Overviews have officially launched, allowing users to convert documents, PDFs, images, and more into short, narrated video explainers. This new capability transforms static sources into visually engaging content, making it easier to absorb complex ideas—especially in education and content creation settings.
The Google NotebookLM Video Overviews feature generates slide‑based video summaries complete with diagrams, key quotes, numbers, and AI narration. Playback options now include skip‑and‑speed controls for better pacing, and the NotebookLM Studio sidebar has been redesigned with tiles for Audio Overviews, Video Overviews, Mind Maps, and Reports.
Seeing all the articles and blog posts about the new video overviews launch but don’t have the time to read them all?
With Video Overviews built on Gemini‑powered NotebookLM, users can seamlessly synthesize up to 50 sources, including YouTube transcripts, PDFs, Slides, and web pages. This multimodal foundation allows generation of concise video summaries—typically 5 to 15 minutes long—with around 90% accuracy in retaining core information from full-length documents.
The launch of Google NotebookLM Video Overviews marks a turning point for explainer video production. Instead of manually designing visuals and scripting narration, creators can now upload materials and let AI do the heavy lifting—automating key scene selection, slide composition, and voiceover generation. This feature significantly reduces time and effort for producing educational videos, product explainers, or internal training modules.
As a result, content creators—from educators to corporate teams—can rapidly iterate explainer concepts without reliance on external designers or video editors. With Gemini‑driven NotebookLM handling multimodal integration and context-aware presentation, the technology democratizes video production and scales knowledge dissemination effortlessly.
This also deepens NotebookLM’s standing as an AI research assistant, evolving it beyond note-taking into a full-fledged multimedia generator. With mobile app integration and flexible Studio outputs, this innovation paves the way for streamlined workflows and sharper audience engagement.
TCS’s mass layoff will affect approximately 2% of its global workforce, equating to over 12,000 employees, primarily at middle and senior levels, over fiscal 2026 (April 2025–March 2026). The Mumbai‑based IT giant, with about 613,000 employees as of June 2025, confirmed this is its largest-ever restructuring.
TCS CEO K. Krithivasan made one thing clear: the mass layoffs are not driven by AI productivity gains or automation. He emphasized that this decision is due to skill mismatches and cases where redeployment of certain associates was not feasible. “No, this is not because of AI giving some 20 percent productivity gains. We are not doing that. This is driven by where there is a skill mismatch, or, where we think that we have not been able to deploy someone”.
Despite training over 550,000 people in basic AI skills and 100,000 in advanced modules, Krithivasan acknowledged challenges in transitioning more senior personnel into tech-heavy roles. He also noted a strategic shift away from traditional waterfall delivery models toward agile, product-centric workflows, which has diminished demand for multiple leadership layers in project management.
The company described this decision as “difficult but necessary”, assuring that impacted associates will receive notice pay, severance packages, extended health insurance, mental health counselling, and outplacement support.
TCS emphasized that the goal is not to reduce headcount arbitrarily, but to align the workforce with future skills and evolving client demands. As Krithivasan put it, “It is linked to feasibility of deployment—not with the aim of reducing headcount”.
In summary, this workforce adjustment reflects TCS’s broader move to become more agile and future-ready—not simply a response to AI replacing jobs. The focus remains on restructuring to match evolving business needs and skill profiles.
