Companies need to develop a sense of curiosity about both the observable trends in the present and the unobserved factors that could significantly influence their futures. While current trends can drive us in certain directions, we also need to imagine possible futures that could either disrupt our industry or offer tremendous opportunities for growth.
To stay ahead of the game, companies should focus on recognising weak signals in the present – subtle hints of emerging trends – and deciding whether to encourage or discourage these signals to avoid undesirable futures and encourage desirable ones. This process is a constant dance between the push of the present (existing trends) and the pull of the future (visions of the future we want to create).
From DSC: Look out Google, Amazon, and others! Nvidia is putting the pedal to the metal in terms of being innovative and visionary! They are leaving the likes of Apple in the dust.
The top talent out there is likely to go to Nvidia for a while. Engineers, programmers/software architects, network architects, product designers, data specialists, AI researchers, developers of robotics and autonomous vehicles, R&D specialists, computer vision specialists, natural language processing experts, and many more types of positions will be flocking to Nvidia to work for a company that has already changed the world and will likely continue to do so for years to come.
NVIDIA just shook the AI and Robotic world at NVIDIA GTC 2025.
CEO Jensen Huang announced jaw-dropping breakthroughs.
Here are the top 11 key highlights you can’t afford to miss: (wait till you see no 3) pic.twitter.com/domejuVdw5
For enterprises, NVIDIA unveiled DGX Spark and DGX Station—Jensen’s vision of AI-era computing, bringing NVIDIA’s powerful Blackwell chip directly to your desk.
Nvidia Bets Big on Synthetic Data — from wired.com by Lauren Goode Nvidia has acquired synthetic data startup Gretel to bolster the AI training data used by the chip maker’s customers and developers.
Nvidia, xAI to Join BlackRock and Microsoft’s $30 Billion AI Infrastructure Fund — from investopedia.com by Aaron McDade Nvidia and xAI are joining BlackRock and Microsoft in an AI infrastructure group seeking $30 billion in funding. The group was first announced in September as BlackRock and Microsoft sought to fund new data centers to power AI products.
AI Super Bowl. Hi everyone. This week, 20,000 engineers, scientists, industry executives, and yours truly descended upon San Jose, Calif. for Nvidia’s annual GTC developers’ conference, which has been dubbed the “Super Bowl of AI.”
The problem is that these new roles demand a level of expertise that wasn’t expected from entry-level candidates in the past. Where someone might have previously learned on the job, they are now required to have relevant certifications, AI proficiency, or experience with digital platforms before they even apply.
Some current and emerging job titles that serve as entry points into industries include:
Digital marketing associate – This role often involves content creation, social media management, and working with AI-driven analytics tools.
Junior AI analyst – Employees in this role assist data science teams by labeling and refining machine learning datasets.
Customer success associate – Replacing traditional customer service roles, these professionals help manage AI-enhanced customer support systems.
Technical support specialist – While this role still involves troubleshooting software, it now often includes AI-driven diagnostics and automation oversight.
Blind Spot on AI — from the-job.beehiiv.com by Paul Fain Office tasks are being automated now, but nobody has answers on how education and worker upskilling should change.
Students and workers will need help adjusting to a labor market that appears to be on the verge of a historic disruption as many business processes are automated. Yet job projections and policy ideas are sorely lacking.
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The benefits of agentic AI are already clear for a wide range of organizations, including small nonprofits like CareerVillage. But the ability to automate a broad range of business processes means that education programs and skills training for knowledge workers will need to change. And as Chung writes in a must-read essay, we have a blind spot with predicting the impacts of agentic AI on the labor market.
“Without robust projections,” he writes, “policymakers, businesses, and educators won’t be able to come to terms with how rapidly we need to start this upskilling.”
We introduce AI co-scientist, a multi-agent AI system built with Gemini 2.0 as a virtual scientific collaborator to help scientists generate novel hypotheses and research proposals, and to accelerate the clock speed of scientific and biomedical discoveries.
There is a speed limit. GenAI technology continues to advance at incredible speed. However, most organizations are moving at the speed of organizations, not at the speed of technology. No matter how quickly the technology advances—or how hard the companies producing GenAI technology push—organizational change in an enterprise can only happen so fast.
Barriers are evolving. Significant barriers to scaling and value creation are still widespread across key areas. And, over the past year regulatory uncertainty and risk management have risen in organizations’ lists of concerns to address. Also, levels of trust in GenAI are still moderate for the majority of organizations. Even so, with increased customization and accuracy of models—combined with a focus on better governance— adoption of GenAI is becoming more established.
Some uses are outpacing others. Application of GenAI is further along in some business areas than in others in terms of integration, return on investment (ROI) and expectations. The IT function is most mature; cybersecurity, operations, marketing and customer service are also showing strong adoption and results. Organizations reporting higher ROI for their most scaled initiatives are broadly further along in their GenAI journeys.
SALT LAKE CITY, Oct. 22, 2024 /PRNewswire/ — Instructure, the leading learning ecosystem and UPCEA, the online and professional education association, announced the results of a survey on whether institutions are leveraging AI to improve learner outcomes and manage records, along with the specific ways these tools are being utilized. Overall, the study revealed interest in the potential of these technologies is far outpacing adoption. Most respondents are heavily involved in developing learner experiences and tracking outcomes, though nearly half report their institutions have yet to adopt AI-driven tools for these purposes. The research also found that only three percent of institutions have implemented Comprehensive Learner Records (CLRs), which provide a complete overview of an individual’s lifelong learning experiences.
In the nearly two years since generative artificial intelligence burst into public consciousness, U.S. schools of education have not kept pace with the rapid changes in the field, a new report suggests.
Only a handful of teacher training programs are moving quickly enough to equip new K-12 teachers with a grasp of AI fundamentals — and fewer still are helping future teachers grapple with larger issues of ethics and what students need to know to thrive in an economy dominated by the technology.
The report, from the Center on Reinventing Public Education, a think tank at Arizona State University, tapped leaders at more than 500 U.S. education schools, asking how their faculty and preservice teachers are learning about AI. Through surveys and interviews, researchers found that just one in four institutions now incorporates training on innovative teaching methods that use AI. Most lack policies on using AI tools, suggesting that they probably won’t be ready to teach future educators about the intricacies of the field anytime soon.
It is bonkers that I can write out all my life goals on a sheet of paper, take a photo of it, and just ask Claude or ChatGPT for help.
I get a complete plan, milestones, KPIs, motivation, and even action support to get there.
As beta testers, we’re shaping the tools of tomorrow. As researchers, we’re pioneering new pedagogical approaches. As ethical guardians, we’re ensuring that AI enhances rather than compromises the educational experience. As curators, we’re guiding students through the wealth of information AI provides. And as learners ourselves, we’re staying at the forefront of educational innovation.
In a groundbreaking study, researchers from Penn Engineering showed how AI-powered robots can be manipulated to ignore safety protocols, allowing them to perform harmful actions despite normally rejecting dangerous task requests.
What did they find ?
Researchers found previously unknown security vulnerabilities in AI-governed robots and are working to address these issues to ensure the safe use of large language models(LLMs) in robotics.
Their newly developed algorithm, RoboPAIR, reportedly achieved a 100% jailbreak rate by bypassing the safety protocols on three different AI robotic systems in a few days.
Using RoboPAIR, researchers were able to manipulate test robots into performing harmful actions, like bomb detonation and blocking emergency exits, simply by changing how they phrased their commands.
Why does it matter?
This research highlights the importance of spotting weaknesses in AI systems to improve their safety, allowing us to test and train them to prevent potential harm.
From DSC: Great! Just what we wanted to hear. But does it surprise anyone? Even so…we move forward at warp speeds.
From DSC:
So, given the above item, does the next item make you a bit nervous as well? I saw someone on Twitter/X exclaim, “What could go wrong?” I can’t say I didn’t feel the same way.
We’re also introducing a groundbreaking new capability in public beta: computer use.Available today on the API, developers can direct Claude to use computers the way people do—by looking at a screen, moving a cursor, clicking buttons, and typing text. Claude 3.5 Sonnet is the first frontier AI model to offer computer use in public beta. At this stage, it is still experimental—at times cumbersome and error-prone. We’re releasing computer use early for feedback from developers, and expect the capability to improve rapidly over time.
Per The Rundown AI:
The Rundown: Anthropic just introduced a new capability called ‘computer use’, alongside upgraded versions of its AI models, which enables Claude to interact with computers by viewing screens, typing, moving cursors, and executing commands.
… Why it matters: While many hoped for Opus 3.5, Anthropic’s Sonnet and Haiku upgrades pack a serious punch. Plus, with the new computer use embedded right into its foundation models, Anthropic just sent a warning shot to tons of automation startups—even if the capabilities aren’t earth-shattering… yet.
Also related/see:
What is Anthropic’s AI Computer Use? — from ai-supremacy.com by Michael Spencer Task automation, AI at the intersection of coding and AI agents take on new frenzied importance heading into 2025 for the commercialization of Generative AI.
New Claude, Who Dis? — from theneurondaily.com Anthropic just dropped two new Claude models…oh, and Claude can now use your computer.
What makes Act-One special? It can capture the soul of an actor’s performance using nothing but a simple video recording. No fancy motion capture equipment, no complex face rigging, no army of animators required. Just point a camera at someone acting, and watch as their exact expressions, micro-movements, and emotional nuances get transferred to an AI-generated character.
Think about what this means for creators: you could shoot an entire movie with multiple characters using just one actor and a basic camera setup. The same performance can drive characters with completely different proportions and looks, while maintaining the authentic emotional delivery of the original performance. We’re witnessing the democratization of animation tools that used to require millions in budget and years of specialized training.
Also related/see:
Introducing, Act-One. A new way to generate expressive character performances inside Gen-3 Alpha using a single driving video and character image. No motion capture or rigging required.
Google has signed a “world first” deal to buy energy from a fleet of mini nuclear reactors to generate the power needed for the rise in use of artificial intelligence.
The US tech corporation has ordered six or seven small nuclear reactors (SMRs) from California’s Kairos Power, with the first due to be completed by 2030 and the remainder by 2035.
After the extreme peak and summer slump of 2023, ChatGPT has been setting new traffic highs since May
ChatGPT has been topping its web traffic records for months now, with September 2024 traffic up 112% year-over-year (YoY) to 3.1 billion visits, according to Similarweb estimates. That’s a change from last year, when traffic to the site went through a boom-and-bust cycle.
Google has made a historic agreement to buy energy from a group of small nuclear reactors (SMRs) from Kairos Power in California. This is the first nuclear power deal specifically for AI data centers in the world.
Hey creators!
Made on YouTube 2024 is here and we’ve announced a lot of updates that aim to give everyone the opportunity to build engaging communities, drive sustainable businesses, and express creativity on our platform.
Below is a roundup with key info – feel free to upvote the announcements that you’re most excited about and subscribe to this post to get updates on these features! We’re looking forward to another year of innovating with our global community it’s a future full of opportunities, and it’s all Made on YouTube!
Today, we’re announcing new agentic capabilities that will accelerate these gains and bring AI-first business process to every organization.
First, the ability to create autonomous agents with Copilot Studio will be in public preview next month.
Second, we’re introducing ten new autonomous agents in Dynamics 365 to build capacity for every sales, service, finance and supply chain team.
10 Daily AI Use Cases for Business Leaders— from flexos.work by Daan van Rossum While AI is becoming more powerful by the day, business leaders still wonder why and where to apply today. I take you through 10 critical use cases where AI should take over your work or partner with you.
Emerging Multi-Modal AI Video Creation Platforms The rise of multi-modal AI platforms has revolutionized content creation, allowing users to research, write, and generate images in one app. Now, a new wave of platforms is extending these capabilities to video creation and editing.
Multi-modal video platforms combine various AI tools for tasks like writing, transcription, text-to-voice conversion, image-to-video generation, and lip-syncing. These platforms leverage open-source models like FLUX and LivePortrait, along with APIs from services such as ElevenLabs, Luma AI, and Gen-3.
Going forward, the opportunity for AI agents will be “gigantic,” according to Nvidia founder and CEO Jensen Huang.
Already, progress is “spectacular and surprising,” with AI development moving faster and faster and the industry getting into the “flywheel zone” that technology needs to advance, Huang said in a fireside chat at Salesforce’s flagship event Dreamforce this week.
“This is an extraordinary time,” Huang said while on stage with Marc Benioff, Salesforce chair, CEO and co-founder. “In no time in history has technology moved faster than Moore’s Law. We’re moving way faster than Moore’s Law, are arguably reasonably Moore’s Law squared.”
“We’ll have agents working with agents, agents working with us,” said Huang.
As we navigate the rapidly evolving landscape of artificial intelligence in education, a troubling trend has emerged. What began as cautious skepticism has calcified into rigid opposition. The discourse surrounding AI in classrooms has shifted from empirical critique to categorical rejection, creating a chasm between the potential of AI and its practical implementation in education.
This hardening of attitudes comes at a significant cost. While educators and policymakers debate, students find themselves caught in the crossfire. They lack safe, guided access to AI tools that are increasingly ubiquitous in the world beyond school walls. In the absence of formal instruction, many are teaching themselves to use these tools, often in less than productive ways. Others live in a state of constant anxiety, fearing accusations of AI reliance in their work. These are just a few symptoms of an overarching educational culture that has become resistant to change, even as the world around it transforms at an unprecedented pace.
Yet, as this calcification sets in, I find myself in a curious position: the more I thoughtfully integrate AI into my teaching practice, the more I witness its potential to enhance and transform education
The urgency to integrate AI competencies into education is about preparing students not just to adapt to inevitable changes but to lead the charge in shaping an AI-augmented world. It’s about equipping them to ask the right questions, innovate responsibly, and navigate the ethical quandaries that come with such power.
AI in education should augment and complement their aptitude and expertise, to personalize and optimize the learning experience, and to support lifelong learning and development. AI in education should be a national priority and a collaborative effort among all stakeholders, to ensure that AI is designed and deployed in an ethical, equitable, and inclusive way that respects the diversity and dignity of all learners and educators and that promotes the common good and social justice. AI in education should be about the production of AI, not just the consumption of AI, meaning that learners and educators should have the opportunity to learn about AI, to participate in its creation and evaluation, and to shape its impact and direction.
86% of students globally are regularly using AI in their studies, with 54% of them using AI on a weekly basis, the recent Digital Education Council Global AI Student Survey found.
ChatGPT was found to be the most widely used AI tool, with 66% of students using it, and over 2 in 3 students reported using AI for information searching.
Despite their high rates of AI usage, 1 in 2 students do not feel AI ready. 58% reported that they do not feel that they had sufficient AI knowledge and skills, and 48% do not feel adequately prepared for an AI-enabled workplace.
The Post-AI Instructional Designer— from drphilippahardman.substack.com by Dr. Philippa Hardman How the ID role is changing, and what this means for your key skills, roles & responsibilities
Specifically, the study revealed that teachers who reported most productivity gains were those who used AI not just for creating outputs (like quizzes or worksheets) but also for seeking input on their ideas, decisions and strategies.
Those who engaged with AI as a thought partner throughout their workflow, using it to generate ideas, define problems, refine approaches, develop strategies and gain confidence in their decisions gained significantly more from their collaboration with AI than those who only delegated functional tasks to AI.
Leveraging Generative AI for Inclusive Excellence in Higher Education — from er.educause.edu by Lorna Gonzalez, Kristi O’Neil-Gonzalez, Megan Eberhardt-Alstot, Michael McGarry and Georgia Van Tyne Drawing from three lenses of inclusion, this article considers how to leverage generative AI as part of a constellation of mission-centered inclusive practices in higher education.
The hype and hesitation about generative artificial intelligence (AI) diffusion have led some colleges and universities to take a wait-and-see approach.Footnote1 However, AI integration does not need to be an either/or proposition where its use is either embraced or restricted or its adoption aimed at replacing or outright rejecting existing institutional functions and practices. Educators, educational leaders, and others considering academic applications for emerging technologies should consider ways in which generative AI can complement or augment mission-focused practices, such as those aimed at accessibility, diversity, equity, and inclusion. Drawing from three lenses of inclusion—accessibility, identity, and epistemology—this article offers practical suggestions and considerations that educators can deploy now. It also presents an imperative for higher education leaders to partner toward an infrastructure that enables inclusive practices in light of AI diffusion.
An example way to leverage AI:
How to Leverage AI for Identity Inclusion Educators can use the following strategies to intentionally design instructional content with identity inclusion in mind.
Provide a GPT or AI assistant with upcoming lesson content (e.g., lecture materials or assignment instructions) and ask it to provide feedback (e.g., troublesome vocabulary, difficult concepts, or complementary activities) from certain perspectives. Begin with a single perspective (e.g., first-time, first-year student), but layer in more to build complexity as you interact with the GPT output.
Gen AI’s next inflection point: From employee experimentation to organizational transformation — from mckinsey.com by Charlotte Relyea, Dana Maor, and Sandra Durth with Jan Bouly As many employees adopt generative AI at work, companies struggle to follow suit. To capture value from current momentum, businesses must transform their processes, structures, and approach to talent.
To harness employees’ enthusiasm and stay ahead, companies need a holistic approach to transforming how the whole organization works with gen AI; the technology alone won’t create value.
Our research shows that early adopters prioritize talent and the human side of gen AI more than other companies (Exhibit 3). Our survey shows that nearly two-thirds of them have a clear view of their talent gaps and a strategy to close them, compared with just 25 percent of the experimenters. Early adopters focus heavily on upskilling and reskilling as a critical part of their talent strategies, as hiring alone isn’t enough to close gaps and outsourcing can hinder strategic-skills development.Finally, 40 percent of early-adopter respondents say their organizations provide extensive support to encourage employee adoption, versus 9 percent of experimenter respondents.
Change blindness — from oneusefulthing.org by Ethan Mollick 21 months later
I don’t think anyone is completely certain about where AI is going, but we do know that things have changed very quickly, as the examples in this post have hopefully demonstrated. If this rate of change continues, the world will look very different in another 21 months. The only way to know is to live through it.
Over the subsequent weeks, I’ve made other adjustments, but that first one was the one I asked myself:
What are you doing?
Why are you doing it that way?
How could you change that workflow with AI?
Applying the AI to the workflow, then asking, “Is this what I was aiming for? How can I improve the prompt to get closer?”
Documenting what worked (or didn’t). Re-doing the work with AI to see what happened, and asking again, “Did this work?”
So, something that took me WEEKS of hard work, and in some cases I found impossible, was made easy. Like, instead of weeks, it takes 10 minutes. The hard part? Building the prompt to do what I want, fine-tuning it to get the result. But that doesn’t take as long now.
The landscape of education is on the brink of a profound transformation, driven by rapid advancements in artificial intelligence. This shift was highlighted recently by Andrej Karpathy’s announcement of Eureka Labs, a venture aimed at creating an “AI-native” school. As we look ahead, it’s clear that the integration of AI in education will reshape how we learn, teach, and think about schooling altogether.
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Traditional textbooks will begin to be replaced by interactive, AI-powered learning materials that adapt in real-time to a student’s progress.
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As we approach 2029, the line between physical and virtual learning environments will blur significantly.
Curriculum design will become more flexible and personalized, with AI systems suggesting learning pathways based on each student’s interests, strengths, and career aspirations. … The boundaries between formal education and professional development will blur, creating a continuous learning ecosystem.
This episode of the Next Big Idea podcast, host Rufus Griscom and Bill Gates are joined by Andy Sack and Adam Brotman, co-authors of an exciting new book called “AI First.” Together, they consider AI’s impact on healthcare, education, productivity, and business. They dig into the technology’s risks. And they explore its potential to cure diseases, enhance creativity, and usher in a world of abundance.
Key moments:
00:05 Bill Gates discusses AI’s transformative potential in revolutionizing technology.
02:21 Superintelligence is inevitable and marks a significant advancement in AI technology.
09:23 Future AI may integrate deeply as cognitive assistants in personal and professional life.
14:04 AI’s metacognitive advancements could revolutionize problem-solving capabilities.
21:13 AI’s next frontier lies in developing human-like metacognition for sophisticated problem-solving.
27:59 AI advancements empower both good and malicious intents, posing new security challenges.
28:57 Rapid AI development raises questions about controlling its global application.
33:31 Productivity enhancements from AI can significantly improve efficiency across industries.
35:49 AI’s future applications in consumer and industrial sectors are subjects of ongoing experimentation.
46:10 AI democratization could level the economic playing field, enhancing service quality and reducing costs.
51:46 AI plays a role in mitigating misinformation and bridging societal divides through enhanced understanding.
The team has summarized their primary contributions as follows.
The team has offered the first instance of a simple, scalable oversight technique that greatly assists humans in more thoroughly detecting problems in real-world RLHF data.
Within the ChatGPT and CriticGPT training pools, the team has discovered that critiques produced by CriticGPT catch more inserted bugs and are preferred above those written by human contractors.
Compared to human contractors working alone, this research indicates that teams consisting of critic models and human contractors generate more thorough criticisms. When compared to reviews generated exclusively by models, this partnership lowers the incidence of hallucinations.
This study provides Force Sampling Beam Search (FSBS), an inference-time sampling and scoring technique. This strategy well balances the trade-off between minimizing bogus concerns and discovering genuine faults in LLM-generated critiques.
a16z-backed Character.AI said today that it is now allowing users to talk to AI characters over calls. The feature currently supports multiple languages, including English, Spanish, Portuguese, Russian, Korean, Japanese and Chinese.
The startup tested the calling feature ahead of today’s public launch. During that time, it said that more than 3 million users had made over 20 million calls. The company also noted that calls with AI characters can be useful for practicing language skills, giving mock interviews, or adding them to the gameplay of role-playing games.
Google Translate can come in handy when you’re traveling or communicating with someone who speaks another language, and thanks to a new update, you can now connect with some 614 million more people. Google is adding 110 new languages to its Translate tool using its AI PaLM 2 large language model (LLM), which brings the total of supported languages to nearly 250. This follows the 24 languages added in 2022, including Indigenous languages of the Americas as well as those spoken across Africa and central Asia.
Gen-3 Alpha Text to Video is now available to everyone.
A new frontier for high-fidelity, fast and controllable video generation.
We have to provide instructors the support they need to leverage educational technologies like generative AI effectively in the service of learning. Given the amount of benefit that could accrue to students if powerful tools like generative AI were used effectively by instructors, it seems unethical not to provide instructors with professional development that helps them better understand how learning occurs and what effective teaching looks like. Without more training and support for instructors, the amount of student learning higher education will collectively “leave on the table” will only increase as generative AI gets more and more capable. And that’s a problem.
From DSC: As is often the case, David put together a solid posting here. A few comments/reflections on it:
I agree that more training/professional development is needed, especially regarding generative AI. This would help achieve a far greater ROI and impact.
The pace of change makes it difficult to see where the sand is settling…and thus what to focus on
The Teaching & Learning Groups out there are also trying to learn and grow in their knowledge (so that they can train others)
The administrators out there are also trying to figure out what all of this generative AI stuff is all about; and so are the faculty members. It takes time for educational technologies’ impact to roll out and be integrated into how people teach.
As we’re talking about multiple disciplines here, I think we need more team-based content creation and delivery.
There needs to be more research on how best to use AI — again, it would be helpful if the sand settled a bit first, so as not to waste time and $$. But then that research needs to be piped into the classrooms far better. .
From DSC: Last Thursday, I presented at the Educational Technology Organization of Michigan’s Spring 2024 Retreat. I wanted to pass along my slides to you all, in case they are helpful to you.
