Cognitive textual and visual reasoning tasks, such as puzzles, series, and analogies, demand the ability to quickly reason, decipher, and evaluate patterns both textually and spatially. While LLMs and VLMs, through extensive training on large amounts of human-curated data, have attained a high level of pseudo-human intelligence in some common sense reasoning tasks, they still struggle with more complex reasoning tasks that require cognitive understanding. In this work, we introduce a new dataset, NTSEBench, designed to evaluate the cognitive multi-modal reasoning and problem-solving skills of large models. The dataset comprises 2,728 multiple-choice questions comprising of a total of 4,642 images across 26 categories sampled from the NTSE examination conducted nationwide in India, featuring both visual and textual general aptitude questions that do not rely on rote learning. We establish baselines on the dataset using state-of-the-art LLMs and VLMs. To facilitate a comparison between open source and propriety models, we propose four distinct modeling strategies to handle different modalities (text and images) in the dataset instances.

We assess the ability of GPT -- a large language model -- to serve as a financial robo-advisor for the masses, by using a financial literacy test. Davinci and ChatGPT based on GPT-3.5 score 66% and 65% on the financial literacy test, respectively, compared to a baseline of 33%. However, ChatGPT based on GPT-4 achieves a near-perfect 99% score, pointing to financial literacy becoming an emergent ability of state-of-the-art models. We use the Judge-Advisor System and a savings dilemma to illustrate how researchers might assess advice-utilization from large language models. We also present a number of directions for future research.

Much has been said about the potential of artificial intelligence (AI) to transform how we live, work, and interact with each other. But we must also draw attention to a less discussed, but equally important, question -- do we have the skills required to develop AI inclusively and use it responsibly? AI adoption is accelerating, and the overall market is expected to be worth $190 billion by 2025. By 2030, AI technology will add $15.7 trillion to global gross domestic product (GDP). AI is everywhere -- whether we're aware of it or not.

Artificial Intelligence is transforming our personal and professional lives. Around the world, governments, companies, and institutions are proclaiming that we are entering the "era of AI" with the rapid development of intelligent devices that can recognize faces and interact via speech, robots that can work alongside people to help automate warehouse logistics and manufacture goods, algorithms that can generate novel photo realistic images and music, computers that can provide decision support to clinicians to help detect cancer more reliably, and so much more. In every feat that AI can do, however, there lurks potential for misuse and the spreading of inequity. AI education can help change that. As computers continue to automate more routine tasks, AI education is a key enabler to future opportunities where success depends increasingly on intellect, creativity, empathy, and having the right skills and knowledge.

As companies increasingly invest in business intelligence and analytical tools, organisations are increasingly unable to derive critical benefits from them. In most cases, this results because of the inflection shift caused by the new oil, Data. Data has transformed how businesses look at their processes and operations. It has changed perspectives and introduced new sources of revenues, insights and competencies. With AI being on the rise, companies are digitally transforming their organisations at a scale never seen before.

Right now, you are reading these words without much thought or conscious effort. In lightning-fast bursts, your eyes are darting from left to right across your screen, somehow making meaning from what would otherwise be a series of black squiggles. Reading for you is not just easy – it's automatic. Looking at a word and not reading it is almost impossible, because the cogs of written language processing are set in motion as soon as skilled readers see print. And yet, as tempting as it is to think of reading as hard-wired into us, don't be fooled.

"Our analysis suggests that up to 30% of UK jobs could potentially be at high risk of automation by the early 2030s, lower than the US (38%) or Germany (35%), but higher than Japan (21%)." They join the many such reports from academics, analysts and authors that filled an entire chapter titled "Sum of all Fears" in my new book, Silicon Collar. "The reality, however, is that when Oxford, MIT, McKinsey, and Gartner talk, the person on the street and even business executives typically just read the headlines, and when all of these big brands agree on something, it solidifies readers' overall impression--in this case, pessimism." The PwC analysis suffers from the same "lab rat" mindset of so many such studies. This comprised US data from the Programme for the International Assessment of Adult Competencies (PIAAC) database, merged with automatability data from FO.

Parents, listen up: Put your kids in engineering and computer science classes. A recent Bureau of Labor Statistics report says "software development skills continue to be the most in-demand" STEM (science, technology, engineering, and math) related jobs in the United States, and the White House projects that there will be over one million unfilled jobs in STEM related fields by 2020. And perhaps one of the easiest ways to encourage this interest is with toys. Toys and kits that are designed to teach kids hacking and basic programming skills abound, and they cater to a range of ages and skill levels. "It's important that we create learning experiences for kids that help to see what's possible for them, what they can do, who they can be, and the changes that they can make to what's around them," said Eric Rosenbaum, who is an electronics kit designer and has a PhD from MIT's Lifelong Kindergarten group.