#adaptable artificial intelligence

[vivaeducationblogs]

AI-Powered Adaptive Learning

𝐖𝐡𝐚𝐭 𝐢𝐟 𝐲𝐨𝐮𝐫 𝐜𝐥𝐚𝐬𝐬𝐫𝐨𝐨𝐦 𝐜𝐨𝐮𝐥𝐝 𝐥𝐞𝐚𝐫𝐧 𝐚𝐛𝐨𝐮𝐭 𝐲𝐨𝐮𝐫 𝐬𝐭𝐮𝐝𝐞𝐧𝐭𝐬 𝐚𝐬 𝐟𝐚𝐬𝐭 𝐚𝐬 𝐭𝐡𝐞𝐲 𝐥𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐢𝐭?

AI-powered adaptive learning is doing just that and even going a step further. Some platforms are now being trained to spot early signs of learning challenges like dyslexia, long before they show up in test scores.

In our latest article, we explore how this technology holds immense power to transform Indian classrooms, the tools making it possible, and what it all means for teachers and students alike.

[coupleofdays]

A fantastic animatic of the famous "Hate" monologue from I Have No Mouth and I Must Scream (specifically, the radio adaptation). Warning for violence, torture, despair, and one seriously deranged artificial intelligence:

[pitch-and-moan]

The Stuff

An AI-penned remake of The Right Stuff, adapted from Tom Wolfe's novel of the same title. Without actors and writers though, it's just a edited version of the original 1983 film intercut with b-roll, and old NASA film strips from the 1950s and '60s.

[artekai]

Why is she so cute

[gwmac]

Does 'Common Sense' Actually Exist?

The Conversation That Started It All A few nights ago, I found myself on the phone with a good friend discussing something entirely unrelated when he casually dropped the phrase, “Well, it’s just common sense.” That simple remark derailed our entire conversation for the next 30 minutes. I asked him to define what he meant by “common sense.” He couldn’t. Not really. He gave some vague examples,…

[mikeaboutmoney]

Future of Creative Jobs: Roger Hooks on Why AI Won't Replace You — Unless You Let It

By Michael Schiano | Featuring insights from the In the Queue Podcast with Mike Schiano What happens to creative jobs in a world run by Artificial Intelligence?That’s the question on everyone’s mind — and Roger Hooks, Creative Director at Super Micro, has a bold answer: “You won’t lose your job to AI. You’ll lose your job to someone who knows how to use it.” In the latest episode of In the…

[govindhtech]

Designed IBM LoRA Adapter Inference Improves LLM Ability

LLMs express themselves faster to new adaptors.

IBM LoRA

IBM Research has modified the low-rank adapter, IBM LoRA, to give Large Language Models (LLM) specialised features at inference time without delay. Hugging Face now has task-specific, inference-friendly adapters.

Low-rank adapters (LoRAs) may swiftly empower generalist big language models with targeted knowledge and skills for tasks like summarising IT manuals or assessing their own replies. However, LoRA-enhanced LLMs might quickly lose functionality.

Switching from a generic foundation model to one customised via LoRA requires the customised model to reprocess the conversation up to that point, which might incur runtime delays owing to compute and memory costs.

IBM Research created a wait-shortening approach. A “activated” IBM LoRA, or “a” LoRA, allows generative AI models to reuse computation they have already done and stored in memory to deliver results faster during inference time. With the increased usage of LLM agents, quick job switching is crucial.

Like IBM LoRA, aLoRAs may perform specialist jobs. However, aLoRAs can focus on base model-calculated embeddings at inference time. As their name indicates, aLoRAs may be "activated" independently from the underlying model at any time and without additional costs since they can reuse embeddings in key value (KV) cache memory.

According to the IBM researcher leading the aLoRA project, “LoRA must go all the way back to the beginning of a lengthy conversation and recalculate it, while aLoRA does not.”

IBM researchers say an engaged LoRA can accomplish tasks 20–30 times faster than a normal LoRA. Depending on the amount of aLoRAs, an end-to-end communication might be five times faster.

ALoRA: Runtime AI “function” for faster inference

IBM's efforts to expedite AI inferencing led to the idea of a LoRA that might be activated without the base model. LoRA adapters are a popular alternative to fine-tuning since they may surgically add new capabilities to a foundation model without updating its weights. With an adapter, 99 percent of the customised model's weights stay frozen.

LoRAs may impede inferencing despite their lower customisation costs. It takes a lot of computation to apply their adjusted weights to the user's queries and the model's replies.

IBM researchers aimed to reduce work by employing changed weights alone for generation. By dynamically loading an external software library containing pre-compiled code and running the relevant function, statically linked computer programs can execute tasks they weren't planned for.

As their name indicates, aLoRAs may be "activated" independently from the underlying model at any time and without additional costs since they can reuse embeddings in key value (KV) cache memory. An LLM configured with standard LoRAs (left) must reprocess communication for each new IBM LoRA. In contrast, different aLoras (right) can reuse embeddings generated by the basic model, saving memory and processing.

Researchers must execute an AI adaptor without task-aware embeddings that explain the user's request to make it act like a function. Without user-specific embeddings, their activated-LoRA prototypes were inaccurate.

However, they fixed that by raising the adapter's rating. The adapter can now extract more contextual indications from generic embeddings to increased network capacity. After a series of testing, researchers found that their “aLoRA” worked like a LoRA.

Researchers found that aLoRA-customized models could create text as well as regular LoRA models in many situations. One might increase runtime without losing precision.

Artificial intelligence test adapter “library���

IBM Research is offering a library of Granite 3.2 LLM aLoRA adapters to improve RAG application accuracy and reliability. Experimental code to execute the adapters is available as researchers integrate them into vLLM, an open-source platform for AI model delivery. IBM distributes regular Granite 3.2 adapters separately for vLLM usage. Some IBM LoRA task-specific enhancements were provided through Granite Experiments last year.

One of the new aLoRAs may reword discussion questions to help discover and retrieve relevant parts. To reduce the chance that the model may hallucinate an answer, another might evaluate if the retrieved documents can answer a question. A third might indicate the model's confidence in its result, urging users to verify their information.

In addition to Retrieval Augmented Generation (RAG), IBM Research is creating exploratory adapters to identify jailbreak attempts and decide whether LLM results meet user-specified standards.

Agent and beyond test time scaling

It has been shown that boosting runtime computing to analyse and improve model initial responses enhances LLM performance. IBM Research improved Granite 3.2 models' reasoning by providing different techniques to internally screen LLM replies during testing and output the best one.

IBM Research is investigating if aLoRAs can enhance “test-time” or “inference-time” scalability. An adapter may be created to generate numerous answers to a query and pick the one with the highest accuracy confidence and lowest hallucination risk.

Researchers want to know if inference-friendly adapters affect agents, the next AI frontier. When a difficult job is broken down into discrete stages that the LLM agent can execute one at a time, AI agents can mimic human thinking.

Specialised models may be needed to implement and assess each process.

[kakief]

Why Samuel the Blacksmith Matters in the Age of Artificial Intelligence (AI)

Today, loads of people view Artificial Intelligence with anxiety and uncertainty, fearing job loss and radical changes to our daily lives. But we have faced similar transitions before. History offers us perspective: every technological revolution has come with disruption and fear, yet it also brought unprecedented opportunity and growth. Let’s journey back to the early 19th century to understand…

[manmishra]

🌟 Hey tech lovers! 🚀 AI-powered Risk-Based Access Control is here to shake up cybersecurity! 🕵️‍♂️ Real-time threat detection? Yes, please! 🔐 Adaptive policies for 2025 & beyond! 💡 Check out the full scoop #AICybersecurity #TechFuture #StaySaf

[business901-blog]

Utilize Customer Data to Personalize Marketing Efforts

[pitch-and-moan]

The Thin Mannequin

An AI-penned remake of the 1934 comedy mystery based on the Dashiell Hammett novel. The whole thing is filmed with Nick and Nora mannequins and subtitles.

[leonbasinwriter]

In the age of AI, authentication shouldn't be a static barrier; it should be an intelligent, adaptive, and engaging experience. Within @leonbasinwriter Intelligence Singularity, access is not simply granted—it's earned through a dynamic interplay with AI itself.

[drchristophedelongsblog]

The integration of artificial intelligence (AI) into medicine is profoundly transforming practices, and this raises important questions about the ability of geriatricians and general practitioners to adapt.

Here is an analysis of the issues

Growing complexity of medicine with AI

Preventive and predictive medicine

AI can analyze huge amounts of data to identify individual risks and predict disease occurrence.

This requires a deep understanding of algorithms and their interpretation.

Diagnosis

AI helps in interpreting medical images, analyzing biological data and detecting complex patterns.

This requires an ability to validate and integrate AI results into the clinical context.

Therapeutic

AI personalizes treatments based on individual patient characteristics.

This involves knowledge of AI-based therapeutic options and an ability to monitor their effectiveness.

Capacity of geriatricians and general practitioners

Continuing education

Continuing education is essential to keep physicians up to date with advances in AI and its applications in medicine.

Interdisciplinary collaboration

Collaboration with AI specialists, data scientists and other healthcare professionals is crucial for effective use of AI.

Decision support tools

AI can provide decision support tools to support physicians in interpreting data and making clinical decisions.

Specificities of geriatrics

Geriatrics, by its holistic nature, is particularly concerned with the management of multiple pathologies and fragility.

AI can be a valuable asset in synthesizing complex data and personalizing care plans.

The role of the general practitioner

The general practitioner, through regular monitoring of the patient, is on the front line to detect changes and refer to specialists.

AI can help refine its diagnosis and monitoring.

In summary

AI represents a challenge, but also an opportunity to improve the care of elderly patients.

Continuing education, interdisciplinary collaboration and the use of decision support tools are essential to enable geriatricians and general practitioners to adapt to this evolution.

General practitioners and geriatricians will have a key role in using AI as a decision-making tool.

Go further

[cyber-soul-smartz]

Learning from Animal Nature: Insights for Personal Growth

Another day of knowledge and learning – a perfect day for me to cover the reason why I do share knowledge. Personally, I have a philosophy that knowledge is power rooted deep within our humanity; the more we learn, the more we grow. I also know that growth and learning mean connecting to what is within and around us. Through my growth journey and background in learning, education, technology, and…

[bloggerpaula]

How European Venture Capitalists Are Adapting to the Deep Tech Boom

The deep tech revolution is reshaping industries across the globe, and Europe is no exception. However, a significant challenge for European Venture Capitalist (VC) firms lies not in the availability of funds but in the ability to identify and support the right deep tech organizations. A knowledge gap among investors has made it difficult to evaluate these emerging technologies effectively, slowing down potential investments.

Bridging the Knowledge Gap in Deep Tech Investment

Unlike traditional startups, deep tech companies operate in complex, research-intensive domains such as artificial intelligence (AI), quantum computing, robotics, cybersecurity, and machine learning. The sheer breadth of these fields makes it challenging for new Venture Capitalist to assess potential investments confidently. Despite this, many Venture Capitalists like Rajat Khare have recognized the potential of deep tech and have already begun making strategic moves.

European VC Firms Leading the Deep Tech Charge

Several forward-thinking VC firms in Europe have stepped up to support the deep tech boom. Angular Ventures, a UK-based firm founded in 2019 by Gil Dibner, focuses on early-stage deep tech enterprises in Europe and Israel. Similarly, Amadeus Capital Partners, headquartered in Cambridge, invests in startups across Europe and Latin America that have the potential to disrupt billion-dollar markets. As their CEO puts it, “We are attracted by companies that can disrupt existing billion-dollar markets, by either cost or performance, and we are supportive over a number of years as the technology is commercialized.”

Challenges in Deep Tech Investments

The difficulty for venture capitalists does not end with understanding deep tech. One of the biggest hurdles is aligning financial returns with the longer development cycles of deep tech startups. Unlike SaaS or MedTech businesses that follow conventional revenue patterns, deep tech startups operate on uncertain timelines and unpredictable market adoption rates.

As Rajat Khare, founder of Boundary Holding, puts it, “The possibility of particular new deep tech businesses succeeding, the best investments to make, or the speed at which their potential will be reached are all unknown at this time. But now, the sector is developing more swiftly than many experts anticipated.”

This sentiment is reflected in a recent survey where 70% of European investors admitted struggling to apply traditional investment metrics—such as annual recurring revenue or customer acquisition costs—to deep tech startups.

A Strategic Shift Toward Long-Term Investment

Despite these challenges, leading investment firms are actively working to adapt their strategies to fit deep tech’s unique growth trajectory. For instance, Boundary Holding, based in Luxembourg, has been consistently backing deep tech startups, recognizing that long-term research and innovation are crucial for future technological breakthroughs.

To succeed in this evolving landscape, VC firms must adopt a problem-solving mindset, similar to how the Internet transformed the IT industry in the 1980s. As more venture capitalists take the time to understand deep tech, refine investment strategies, and adjust financial expectations, Europe is poised to become a powerhouse for deep tech innovation.

The increasing interest in deep tech by European venture capitalists signals a shift toward sustainable, high-impact investments, ensuring that the region remains at the forefront of the next technological revolution.