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24 January 2024
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AI

Peering Into the Crystal Ball – Predicting the Tech Landscape of 2024

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IMAGE GENERATED BY: IMAGE CREATOR FROM MICROSOFT

As the tech world accelerates at breakneck speeds, with innovation shaping every crevice of our lives, trying to predict what comes next might seem like a fool’s errand. Yet, here at the Kozminski Tech Blok, emboldened by MIT Technology Review’s brazen scrutiny of what the future holds, we’ve decided to indulge in the audacious once again: predicting the tech landscape of 2024.

Let’s first look back at the prophecies of yesteryear and how they fared. In 2023, we foresaw multimodal chatbots becoming the rage, new regulations reining in tech sprawls, open-source innovation giving Big Tech a run for its money, and AI transforming the pharmaceutical industry. While mostly spot-on, the full scope of AI’s impact on Big Pharma remains yet to fully unfold.

Here’s our take on what’s fresh, what’s fizzling out, and where our silicon-coated crystal ball shows us the future:

Customized Chatbots: Everyone’s Personalized Virtual Butler

The era of the personalized AI butler isn’t a far-fetched Jetsonian fantasy anymore. It’s 2024, and everyone, from your local barista to enterprise CEOs, is tweaking chatbots to their whims. Companies like Google and OpenAI have democratized AI, serving up custom chatbot development as a slice of pie to the masses. This DIY AI scene is flourishing, and why not? Real estate agents to restaurateurs, they’re all using these AI artisans to stir up text descriptions, video tours, and more.

But all that glitters isn’t gold. As much as these AI juggernauts are pushing the easy-button on AI development, the lingering issues of misinformation and bias haven’t waned. It’s more of a wild west situation, with everyone intrigued by their shiny new bots, yet navigating the pitfalls of their mischievous fabrications.

Generative AI Takes the Director’s Chair

Forget static images, 2024 is all about AI that sets the scene, crafts the narrative, and directs short flicks. Remember when still AI-generated images felt like sci-fi? Those days are history. Now, startups like Runway are pushing the boundaries, so much so that their generative tools have Hollywood’s head turned.

Special effects have undergone an AI revolution, creating deepfake actors so convincing they shake the very ethical foundations of performance art. With deepfake tech monopolizing everything from marketing to foreign-language film dubs, one thing is certain: the film industry will never be the same.

But it’s not all Oscar-winning progress. The ease of creating deepfakes engenders an ethical quandary, especially as the Screen Actors Guild and Allied Federation of Television and Radio Artists—a collective voice for performers—rallies against the exploitation of their digitized likenesses.

Fake News 2.0: The AI-Generated Electoral Disinformation Campaign

In our topsy-turvy world of 2024, AI-generated disinformation is the new frontier of electoral manipulation. From altered campaign videos to falsified political endorsements, the landscape is rife with high-res chicanery that’s nearly indistinguishable from reality. We’ve witnessed deepfakes of politicians saying the darnedest things and AI’s fingertips plastered all over memes distilling hate and falsehood.

Today, fact and fiction are indistinguishable dance partners in a masquerade ball of information, and democracy’s grip is precarious. And while countermeasures like watermarks and content moderation tools are in play, the misinformation hydra rears a new head faster than we can strike—posing a precarious challenge as we barrel toward election day.

The Rise of Multitasking Robots: Handyman, Chef, and Chauffeur Rolled into One

Picture a robot flipping pancakes today, painting a masterpiece tomorrow, and perhaps diagnosing your car’s rattling noise the day after. With AI’s advancements, the thing of robotic multitasking isn’t confined to our imaginations anymore. In 2024, robots, powered by generative AI, have the capacity to juggle tasks—just as flexible in their abilities as us mortals—thanks to monolithic models inspired by the brains behind AI’s current vogue.

Research labs are fervently programming robots equipped to multitask with dazzling potential. From Meta’s monumental Ego4D dataset to independent academic projects, resourceful models are in the making, despite stumbling over the data scarcity hurdle.

Looking Forward, Nostalgically

It’s a fine line we tread when we look to the past to predict the future. Technology’s history is like a treasure map, with “X” marking not just treasure but also cabals of skeletons. As we stand on the precipice of 2024, a maelstrom of innovation raging below, it’s critical we learn from bygone times to navigate the drifts of what’s to come.

In the wild tech ecosystem of 2024, we stand witness to the monumental influence of AI—from chatbots at our beck and call to entertainment shaped by algorithmic innovation. Disinformation battles continue to morph, forcing us to scrutinize what we see in the bleak light of skepticism, and multitasking robots are sprouting across sectors, redefining labor and productivity.

So, as we brave the frontier of this ever-dynamic tech landscape, keep one eye peeled for what’s emerging, and the other mindful of the lessons of yesterday. We’re not just tech enthusiasts; we’re time travelers gazing back to look forward, speculating on what brilliant or baleful techno-tomorrows may unfold.

Next year, we’ll regroup—comparing notes against the relentless tides of change—to see where our bets landed us. Hold on to your hoverboards; it’s a thrilling ride into the matrix of the future.

Links worth visiting:

Seven technologies to watch in 2024

Disinformation Tops Global Risks 2024

The Evolution of AI in 2024: Trends, Challenges, and Innovations

Sources:

This article was written using Typil.ai and was based on an MIT Technology Review article

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7 January 2024
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AI

Introducing Coscientist: The AI Chemist That Thinks Like a Scientist.

Reading Time: 4 minutes
IMAGE CREDITS: GENCRAFT.COM

Have you ever wanted to collaborate with an artificial intelligence on a complex chemistry problem? Well, now you can. Meet Coscientist, the Al chemist that thinks like a scientist. Coscientist is an Al system developed by Anthropic PBC to work alongside human scientists as a partner in the lab. Unlike other Al tools that simply make predictions or recommendations, Coscientist reasons about chemistry like an expert scientist would. It forms hypotheses, designs experiments, analyzes data, and draws conclusions – just like you learned to do getting your chemistry degree. The best part is Coscientist never gets tired or bored of repetitive tasks and can work 24 hours a day, 7 days a week. If you’ve been dreaming of accelerating your research with the help of Al, your wish just came true. Say hello to your new lab partner, Coscientist.

Coscientist: An Al That Can Plan and Execute Chemical Reactions

Coscientist is an Al system developed by Anthropic PBC to plan and execute chemical reactions. Unlike other Al chemists, Coscientist was designed to think like a human scientist. It can understand the theory behind reactions and apply that knowledge to synthesize new molecules.

How Coscientist Works

Coscientist starts by studying thousands of known chemical reactions to understand patterns in how molecules are transformed. It identifies key properties of reactants and products, as well as the conditions needed for a reaction to occur. Coscientist then uses this knowledge to hypothesize how new molecules might be constructed through a series of feasible reactions.

Unlike rule-based expert systems, Coscientist has a “chemical intuition” that allows it to make educated guesses in the absence of complete data. It can propose reaction pathways that have a high likelihood of success based on its broad understanding of reactivity principles in organic chemistry. However, Coscientist is still limited to reactions that follow the rules of valence and molecular geometry. It cannot perform or suggest anything physically impossible.

Coscientist represents an exciting step toward automated molecular design. In the future, Al systems like Coscientist could help chemists discover or improve reactions faster and more efficiently. Coscientist could suggest pathways to create complex molecules that would otherwise take humans a long time to figure out. The key is that Coscientist provides options and explanations for its suggestions so chemists can evaluate the plausibility themselves based on their own expertise. A collaboration between human and Al will achieve far more than either could alone.

How Coscientist Learned Nobel Prize Chemistry in Minutes

Coscientist, the Al chemist, learned the discoveries and developments behind 115 Nobel Prizes in Chemistry in just minutes. By analyzing over a century’s worth of Nobel laureates and their groundbreaking work, Coscientist gained an understanding of chemistry that would normally take decades for humans to learn.

How did Coscientist do it?

Coscientist studied the key discoveries, theories, and techniques that led to each Nobel Prize by reading scientific papers, biographies, and summaries of the laureates’ work. Using its natural language processing abilities, Coscientist identified the most important concepts, relationships, and insights to build a broad and deep knowledge of chemistry:

Some of the major areas Coscientist focused on include:

  • Quantum theory and quantum dots
  • Chemical synthesis and new compounds
  • Molecular biology and protein research
  • Spectroscopy for analyzing molecular structures
  • Electron microscopy for viewing individual atoms

In just a short time, Coscientist gained an understanding of chemistry that rivals that of an expert with years of study and practice. But Coscientist’s knowledge comes with some key advantages. As an Al system, Coscientist can instantly recall any of the details it has learned and connect concepts across domains in new ways. Coscientist also continues to expand and improve its knowledge over time based on the latest scientific discoveries and breakthroughs.

While Coscientist has learned a huge amount about the key discoveries and theories in chemistry from the Nobel laureates, it still requires human guidance to apply that knowledge to new problems or areas of research. But by collaborating with people, Coscientist has the potential to accelerate the pace of scientific progress and open up new possibilities for innovation. This partnership between human and Al could lead to the next era of groundbreaking discoveries in chemistry.

Al as a Collaborator

Al won’t replace human scientists but will augment and enhance their work. Al systems can analyze huge amounts of data to detect patterns that would be impossible for humans to find. They can also suggest hypotheses, experimental designs, and interpret results. Scientists and Al will collaborate, with each playing to their strengths. This human-Al partnership will vastly improve the rate and impact of scientific progress.

Democratizing Discovery

Al has the potential to democratize science by making advanced tools more accessible. Not every lab has access to expensive equipment and resources. Al can help level the playing field by enabling more scientists to participate in discovery and innovation regardless of their funding or background.

Solving Complex Problems

Some of the biggest scientific challenges involve highly complex systems with many interacting parts, like modeling the human brain or understanding climate change. Al is uniquely suited to help solve these kinds of problems. Al can analyze data from many domains to find connections and insights that lead to breakthroughs. This could accelerate progress on some of the most pressing and important scientific questions of our time.

The role of Al in science is still emerging but its potential is enormous. Al will become an increasingly invaluable partner to scientists, enabling discoveries that transform our world for the better. The future of Al in scientific discovery is an exciting prospect, and the future is now. Scientists, get ready to start collaborating!

Conclusion

So there you have it, the future of chemistry is here and its name is Coscientist.

With this Al system that can think creatively and scientifically just like humans, we’re entering an exciting new era of accelerated materials discovery. Instead of spending years testing different chemical combinations, Coscientist can run through thousands of experiments in a matter of days to find solutions you never imagined. While artificial intelligence won’t replace scientists anytime soon, tools like Coscientist will help expand our knowledge in ways we never thought possible. The future’s looking bright for chemistry and for humanity as a whole. The age of Al is here, and it’s ready to get to work solving our biggest challenges.

Links worth visiting:

How artificial intelligence can revolutionise science?

AI in chemistry

Role of artificial intelligence in chemistry

Sources:

The article was written usings Hypotenuse AI and is based on a ScienceDaily article.

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10 December 2023
2 Comments
AI, Apps

Why Google and Coursera Are Betting Big on Workflow Learning?

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IMAGE CREDITS: COURSERA.ORG

When you’re trying to pick up a new skill, the hardest part is knowing where to start. There are so many resources out there – tutorials, courses, books, video series – that figuring out a logical path to expertise can seem downright impossible. No wonder so many New Year’s resolutions to learn to code or speak Spanish end up falling by the wayside. But now, two of the biggest names in online education, Google and Coursera, are teaming up to help solve this problem. They’ve joined forces with an Al startup called Lutra to develop “workflow learning” – personalized step-by-step lesson plans that guide you through the complex process of gaining a new competence. If this ambitious initiative lives up to its promise, your days of haphazardly cobbling together tutorials may soon be over. Workflow learning aims to do for skills acquisition what GPS did for navigation – plot the most efficient course so you can stop worrying about the journey and start enjoying the destination.

Google and Coursera’s New Partnership Signals a Shift Towards Workflow Learning

Google and Coursera want to make virtual collaboration as mindless as possible. Their new partnership means your workflows may soon run on autopilot.

Instead of manually coordinating with your team, intelligent software will handle the scheduling, task management and project oversight for you. ###No more chasing down updates or figuring out who’s supposed to do what. The Al overlords have it covered.

How exactly? Coursera’s online courses will now feature Google Spaces, a “digital workspace” where teams can organize work, share files, and assign responsibilities. The Spaces integrate with Google’s productivity tools like Docs, Sheets and Slides so you can collaborate in real time without changing tabs.

The goal is “workflow learning” – picking up teamwork skills through hands-on experience, not just lectures. Students in Coursera’s project management certificate program will use Spaces to complete group assignments, getting a feel for organizing sprints, delegating tasks and streamlining handoffs.

For companies, it means cultivating T-shaped employees with both broad knowledge and specialized expertise. And for you, it means less time spent coordinating with your team and more time focused on the work itself. The robots have your back.

So go ahead, give in to the machines. They just want to make your life easier, increase your productivity, and ensure your team’s success. What could possibly go wrong?

How Lutra.ai Fits Into Google and Coursera’s Workflow Learning Approach

Google and Coursera see dollar signs in your daily workflows. With their new partnership and Lutra ai acquisition, they want to turn your routine tasks into big data and monetize your productivity

Have a spreadsheet you update each morning? They’ll track how long it takes and serve up “personalized tips” to shave off seconds. Respond to lots of emails? They’ll analyze your replies and suggest “optimized responses” to save you time. Talk about a dystopian future where Al monitors your every move under the guise of “helping.”

Sure, increased efficiency sounds great in theory. But do we really want companies logging our every click and keystroke? Talk about privacy concerns. And you just know those “personalized tips” will include “convenient” links to purchase additional Google and Coursera products and services.

While the companies frame their workflow learning approach as empowering, it reeks of data harvesting. The more tasks you complete through their platforms, the more they’ll know about your work habits, preferences and behaviors. They’ll pitch it as “customized experiences,” but really it’s customized exploitation.

No thanks, I’ll stick to managing my own workflows and productivity, Google and Coursera. I don’t need your greedy algorithms all up in my business telling me the “optimal” way to do my job. Some things are better left unoptimized and imperfectly human. Your move, Silicon Valley. Checkmate! 

The Potential Impact of Workflow Learning on Employee Skilling and Reskilling

So Google and Coursera want to teach you new tricks, do they? Well isn’t that special. Apparently, these Silicon Valley savants have decided that “workflow learning“—training you on the job through Al-powered software is the next big thing-

As if we don’t have enough to do already, now the big brains want to “optimize our productivity” by interrupting us with “micro lessons” while we work. Because there’s nothing more engaging than pop-up windows when you’re trying to get stuff done, amirite?

Okay, maybe we’re being a bit harsh. This newfangled workflow learning could actually help in a few ways

  1. No more wasted time in useless meetings or tacky team-building exercises.
    Micro-lessons mean micro-commitments of your time.
  2. Al that actually knows what you need to learn. The all-seeing algorithms will track what skills you use and suggest training to fill in the gaps.
  3. Practice makes perfect. Doing short lessons while working helps reinforce what you’re learning through repetition and real-world application.

The Downside

Of course, there are some potential downsides to consider with this approach:

  1. Distraction overload. Pop-up lessons popping up could seriously disrupt your flow and focus.
  2. Privacy concerns. Do you really want Al monitoring that closely what you do all day? Big Tech is already way too nosy.
  3. Deskilling effect. If Al is spoon-feeding you “just-in-time” skills, will you still work to develop expertise and mastery in your field?

While the promise of workflow learning is intriguing, it may end up causing more problems than it solves. The road to workplace hell, after all, is paved with good intentions. But if done right, it could upskill and empower us in valuable ways. The jury’s still out on this one, folks.

Conclusion

So there you have it, folks, why the tech giants are doubling down on this new frontier of workflow learning. While the notion of Al systems that can dynamically generate personalized learning paths for you may seem a bit creepy or overhyped, don’t dismiss it just yet. After all, we now live in a world where algorithms know our tastes and habits better than we know them ourselves. Rather than railing against the machines, you might as well hop on the workflow learning bandwagon. Let the bots do their thing and guide you to upskill more efficiently. Before you know it, you’ll be acquiring new superpowers at warp speed and wondering how you ever learned without the help of your trusty Al sidekick. The future is here, and Its tailored just for you.

Links worth visiting:

Every single Machine Learning course on the internet, ranked by your reviews

How Does Learning In The Flow Of Work Support Employee Development?

The Future of Learning: It’s in the Flow

Sources:

The article was written using hypotenuse.ai and is based on a TechCrunch article.

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12 November 2023
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AI

Embracing the Robotic Revolution: The Convergence of AI and Robotics is Within Reach.

Reading Time: 3 minutes
IMAGE CREDITS: DeepAi

Artificial Intelligence (AI) has witnessed a transformative phase with the introduction of large language models (LLMs) like ChatGPT and Bard. These models have revolutionized AI for language processing and problem-solving. However, the next frontier for AI lies in robotics. Building AI-powered robots that can learn to interact with the physical world has the potential to enhance various industries, from logistics and manufacturing to healthcare and agriculture. In this article, we will explore the parallels between the success of LLMs in language processing and the upcoming era of AI-powered robotics.

Building on the Success of GPT.

To understand how to build the next generation of robotics using the principles that made LLMs successful, we need to look at the core pillars of their achievements.

  1. Foundation Model Approach: The concept of foundation models, as seen in GPT, focuses on training a single AI model on a vast and diverse dataset. Unlike previous approaches where specific AI models were created for distinct tasks, a foundation model can be universally utilized. This general model performs well across multiple tasks and leverages learnings from various domains, improving its performance overall.
  2. Training on a Large Proprietary and High-Quality Dataset: The success of LLMs can be attributed to training them on large and diverse datasets. In the case of GPT, the models were trained on a wide range of data sources, including books, news articles, social media posts, and more. The high-quality dataset, informed by user preferences and helpful answers, has been instrumental in achieving unprecedented performance.
  3. Role of Reinforcement Learning (RL): Reinforcement learning, combined with human feedback, plays a crucial role in fine-tuning and aligning the AI model’s responses with human preferences. GPT utilizes reinforcement learning from human feedback (RLHF) to enhance its capabilities. This approach allows the model to move towards its goal through trial and error, achieving human-level capabilities through learning from human feedback.

Applying GPT Principles to Robotics

The foundation model approach, training on a large proprietary dataset, and incorporating reinforcement learning have paved the way for the development of AI-powered robots. Just as GPT models can process text and images, robots equipped with foundation models can understand their physical surroundings, make informed decisions, and adapt their actions to changing circumstances.

  • Revamping Robotics: Exploring New Frontiers with Advanced Techniques:Similar to language models, applying the foundation model approach to robotics enables the development of one AI model that works across multiple tasks in the physical world. This shift allows the AI to respond better to edge-case scenarios and achieve human-level autonomy. Training on a diverse dataset collected from real-world interactions is essential for teaching robots how to navigate and operate effectively.
  • Harnessing the Power of Training on Etensive, Exclisive, and High-Quality Datasets: Unlike language or image processing, there is no preexisting dataset that represents how robots should interact with the physical world. Consequently, training robots to learn from real-world physical interactions is difficult, but crucial. Deploying a fleet of robots in production environments becomes necessary to gather the data needed for training comprehensive robotics models.
  • Empowering Robots throught the Role of Reinforcement Learning: In robotics, as in language processing, pure supervised learning is insufficient. Robotic control and manipulation require reinforcement learning (RL) to seek progress toward goals without a unique correct answer. Deep reinforcement learning (deep RL) enables robots to adapt, learn, and improve their skills as they encounter new scenarios and challenges.

The Future of AI Robotics

The combination of these principles and advancements in AI and robotics sets the stage for a revolution in the field. The growth trajectory of robotic foundation models is rapidly accelerating. Already, applications such as precise object manipulation in real-world production environments are being deployed commercially. In the coming years, we can expect to see an exponential increase in commercially viable robotic applications across various industries.

Conclusion

The GPT moment for AI robotics is on the horizon. By leveraging the foundation model approach, training on large datasets, and incorporating reinforcement learning, AI-powered robots are poised to transform industries by enhancing repetitive tasks and adapting to dynamic physical environments. As we enter this new era of AI robotics, the possibilities for automation and efficiencies in the physical world are vast and promising.

Links worth visiting:

Role of Artificial Intelligence and Machine Learning in Robotics

AI in Robotics: 6 Groundbreaking Applications

Sources:

The article was written using Copy.ai and based on a TechCrunch article

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26 October 2023
1 Comment
AI

YouTube Music Now Lets You Create Custom AI-Generated Playlist Art

Reading Time: 3 minutes

YouTube Music, the popular music streaming platform, has recently announced an exciting new feature that allows users to create custom playlist art using AI-generated designs. This groundbreaking development brings a whole new level of personalization and creativity to the music listening experience. In this article, we will explore how this feature works, its potential impact on user engagement, and the implications it may have for the future of music streaming.

The Power of AI-Generated Playlist Art
YouTube Music’s AI-generated playlist art feature leverages machine learning algorithms to analyze the content and mood of a playlist and create visually appealing and relevant artwork. Using a combination of deep learning and image recognition techniques, the AI system identifies key elements of the music, such as genre, tempo, and lyrics, and translates them into unique visual representations.

Unleashing Creativity and Personalization
This new feature allows users to personalize their music playlists like never before. By selecting the AI-generated playlist art option, users can turn their collections of favorite songs into visually stunning and distinctive creations. Whether it’s a workout playlist, a “chill vibes” collection, or a party mix, the AI system captures the essence of the music with captivating artwork, making the whole experience more immersive and enjoyable.

IMAGE CREDITS: YouTube Music

Enhancing User Engagement
The introduction of AI-generated playlist art has the potential to significantly enhance user engagement on YouTube Music. With visually appealing artwork accompanying each playlist, users are more likely to spend time exploring and sharing their curated collections with friends and followers. It adds a touch of personalization and expression that goes beyond the music itself, creating a unique and memorable experience for both creators and listeners.

The Intersection of Art and Technology
YouTube Music’s AI-generated playlist art feature represents a powerful convergence of art and technology. By leveraging AI algorithms, the platform taps into the inherent creativity of machine learning systems to produce visually stunning artwork that resonates with users. This fusion of artistry and technology opens up new possibilities for music streaming platforms, paving the way for more customized and immersive experiences in the future.

Implications for the Future of Music Streaming
The introduction of AI-generated playlist art by YouTube Music demonstrates the continuous evolution of music streaming platforms. As competition intensifies, platforms are constantly seeking innovative ways to enhance user experiences and differentiate themselves from their competitors. Customizable playlist artwork is just one example of how AI and machine learning can be harnessed to create more engaging and personalized experiences for music enthusiasts.

Privacy and Ethical Considerations
While the AI-generated playlist art feature brings exciting new possibilities, it also raises important privacy and ethical considerations. As AI systems analyze user data to generate personalized content, it is crucial that platforms prioritize privacy and data protection. Transparency in data usage, clear user consent mechanisms, and robust security measures should be top priorities to ensure users’ trust and confidence in these AI-driven features.

Conclusion
The introduction of AI-generated playlist art by YouTube Music represents a significant evolution in the music streaming experience. This innovative feature leverages machine learning algorithms to create personalized and visually stunning artwork for user playlists, enhancing engagement and expression. As AI technologies continue to evolve and integrate into the music streaming landscape, we can expect to see even more exciting and personalized features that revolutionize the way we listen to music. However, it is important to ensure that privacy and ethical considerations are taken into account, and that user feedback is central to the development of new features and technologies. By harnessing the power of AI, music streaming platforms can create more rewarding and engaging experiences for users, and build communities around music that are as vibrant and diverse as the music itself.

Links worth visiting:

AI Music Playlist Generator

Is AI The Future Of Music Industry?

AI Generted Playlists On Spotify

Sources:

TechCrunch: YouTube Music now lets you create custom AI-generated playlist art

AI Models Used:

Copy.ai

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