Why We Need to Re-digitize Amharic PDFs for Better AI

A few years ago, we as Ethiopians took an important digital leap by scanning and uploading thousands of Amharic books and documents into PDF format. At the time, it was a great step toward preserving and sharing our literature. But today, many of these files are just pictures — not real, readable text for machines.

This creates a problem for modern AI systems, especially Large Language Models (LLMs), which need actual text to learn and generate fluent responses. These image-based PDFs are invisible to AI, making it hard for Amharic to benefit from the latest advances in technology.

The solution? We need to convert these scanned images into searchable, editable text using Optical Character Recognition (OCR). With new AI-powered OCR tools that support the Ge’ez script, it’s now possible to extract high-quality Amharic text from old PDFs.

This isn’t just about better access — it’s about preparing Amharic for the future. If we want more accurate, fluent, and culturally aware AI tools in our language, we must unlock our digital archives.

🚀 xAI Supercharges Grok Voice Assistant! 🎙️🤖

Elon Musk’s xAI just dropped 3 powerful updates for Grok:

🔍 Grok Vision – Real-time screen commentary

🗣️ Multilingual Audio Output

🌐 Live Search in Voice Mode

📱 Available now on iOS.

📲 Android users with SuperGrok get multilingual & real-time search too!

This move heats up the race with Google & OpenAI as xAI rolls out its sharpest model yet — Grok 3 mini.

💡Smarter, faster, and more intuitive than ever.

When most people try something and fail, what they have to do is not look for an easy way out. Of course, who would hate to have one?

There is always a lesson in competition. Whether we win or not, we learn. Skill grows in the process. master your profession and push the limits!

Hey AI employee are Coming...

Introduction

Quantum computing has long promised to solve problems beyond the reach of classical computers. Recent advancements are bringing this vision closer to reality, with a major breakthrough from D-Wave Quantum Inc. The company’s latest research, published in Science, claims to establish quantum computational supremacy in a real-world application.

The study demonstrates how superconducting quantum annealing processors can efficiently simulate quantum materials, outperforming leading classical methods by an extraordinary margin. D-Wave’s Advantage2 processor completed simulations in minutes — tasks that would take classical supercomputers nearly a million years to process, consuming more energy than the world produces annually.

While this milestone is being hailed as a landmark achievement, it has also sparked debate among experts about the true extent of its impact. This article explores the key findings, the implications for quantum computing, and the ongoing skepticism surrounding the claim.

Quantum Annealing: A Leap Forward in Computing

How Quantum Annealing Works

Quantum annealing (QA) is a computing technique that leverages quantum fluctuations to find optimal solutions to complex problems. In this study, researchers used superconducting QA processors to simulate the continuous-time quantum dynamics of the transverse-field Ising model (TFIM).

To benchmark performance, the study compared QA against leading classical methods, including:

• Tensor networks (Matrix Product States (MPS) and Projected Entangled Pair States (PEPS))
• Neural networks (Neural Quantum States (NQS))

Results showed that QA achieved significantly faster and more accurate simulations, reinforcing the potential of quantum processing units (QPUs) in tackling problems that classical computers struggle with.

Quantum Supremacy on a Practical Problem

Quantum supremacy — the moment a quantum computer outperforms the best classical supercomputers — has been a long-standing goal. Until now, most claims of quantum supremacy have focused on abstract problems with little practical value. D-Wave’s latest research marks a shift by demonstrating quantum supremacy in a real-world application: simulating the quantum behavior of magnetic materials.

Using its Advantage2 quantum processor, D-Wave successfully simulated programmable spin glasses — complex magnetic materials with applications in medical imaging and superconductors. A classical supercomputer would take nearly a million years and more energy than the world produces annually to complete these simulations. D-Wave’s quantum system finished them in minutes.

Beyond Materials Science: A Wider Impact

Magnetic materials power modern technology, from MRI machines to electric motors. However, simulating their quantum behavior requires immense computational power — far beyond the capabilities of classical computers.

D-Wave’s results suggest that quantum annealing could accelerate materials discovery and drive breakthroughs in fields like artificial intelligence and optimization. As Dr. Andrew King, Senior Distinguished Scientist at D-Wave, explains, “Through D-Wave’s technology, we can create and manipulate programmable quantum matter in ways that were impossible even a few years ago.”

Scientific Validation and Market Impact

The research, conducted in collaboration with international scientists, involved simulating quantum dynamics across different lattice structures and sizes. The Advantage2 prototype, with its advanced fast anneal feature, enabled precise calculations that classical methods could not replicate.

The announcement had immediate market effects:

• D-Wave’s stock rose by 10% following the publication.
• Other quantum computing firms, such as IonQ, Rigetti Computing, and Arqit Quantum, also saw stock price gains.

This reflects increasing investor confidence in quantum technology’s potential.

The Skepticism: Is It Really Quantum Advantage?

Despite the excitement, some experts remain skeptical, arguing that D-Wave’s claims may be overstated. Even before the Science paper was published, researchers had begun challenging its findings.

Two independent studies provided counterarguments:

• A team demonstrated that similar calculations could be completed in just two hours on a standard laptop.
• Another study showed that a problem D-Wave claimed would take centuries on a supercomputer could be solved in days with fewer resources.

D-Wave’s Andrew King defended the research, emphasizing that these classical methods did not replicate the full scope of Advantage2’s experiments. “They didn’t do all the problems that we did. They didn’t do all the sizes, all the observables, or all the simulation tests we did.”

Key Findings

1. Quantum Speedup — D-Wave’s Advantage2 processor solved problems in minutes that would take classical supercomputers millions of years.

2. Energy Efficiency — The quantum simulations consumed significantly less energy, highlighting potential for sustainable computing.

3. Scalability — Improved qubit coherence and connectivity allowed the system to handle larger, more complex problems.

4. Beyond-Classical Computation — The study simulated quantum critical dynamics on over 5000 qubits, with results closely matching theoretical predictions.

5. Superior Performance — Quantum annealers outperformed tensor networks and neural network-based classical methods in both accuracy and efficiency.

The Bigger Picture: Scientific Breakthrough or Quantum Hype?

D-Wave’s achievement builds on 25 years of research, following earlier milestones published in Nature Physics (2022) and Nature (2023). Dr. Mohammad Amin, Chief Scientist at D-Wave, called it a step toward realizing Richard Feynman’s vision of simulating nature on a quantum computer.

However, skepticism remains. Some experts argue that claims of quantum advantage are often driven by market pressures rather than scientific rigor. Independent researchers, such as Giuseppe Carleo of EPFL, have challenged D-Wave’s findings, noting that classical methods were not fully exhausted in comparisons.

The debate highlights a broader concern in the quantum computing community: corporate-backed research dominates high-profile journals, often sidelining dissenting voices. As Carleo warns, “Claims of beating ‘all classical methods’ are very hard to justify scientifically. It’s humanly impossible to test every state-of-the-art classical method to show they are truly inadequate.”

Why This Matters: The Future of Quantum Computing

Quantum computing has the potential to revolutionize industries by solving problems that classical systems cannot tackle, from designing new pharmaceuticals to optimizing supply chains. However, unchecked hype risks misleading investors, inflating expectations, and eroding public trust.

To ensure meaningful advancements, the field must balance innovation with transparency. The rush to claim quantum advantage should not overshadow the rigorous scientific process required to validate these breakthroughs.

Conclusion: A New Era of Computing, But Caution is Key

D-Wave’s demonstration of quantum supremacy on a practical problem marks a milestone in the field, proving that quantum systems can outperform classical computers in real-world applications. This breakthrough has the potential to accelerate scientific discovery, transform industries, and drive future innovation.

However, the achievement also underscores the importance of scientific integrity. While quantum computing continues to progress, researchers, investors, and the public must remain vigilant in distinguishing genuine advancements from overhyped claims.

As D-Wave’s CEO Alan Baratz states, “We are thrilled that D-Wave customers can use this technology today to realize tangible value from annealing quantum computers.” The future of quantum computing is here, but its true impact remains to be fully understood.

References

https://www.science.org/doi/10.1126/science.ado6285

Beyond Classical: D-Wave First to Demonstrate Quantum Supremacy on Useful, Real-World Problem

New landmark peer-reviewed paper published in Science, "Beyond-Classical Computation in Quantum Simulation,"…

www.dwavequantum.com

https://www.scientificamerican.com/article/are-d-waves-claims-of-quantum-advantage-just-quantum-hype/

Apple Officially Cancels Its Electric Car Project

After years of rumors, Apple has officially canceled (https://www.bloomberg.com/news/articles/2024-02-28/apple-cancels-its-electric-car-in-favor-of-ai-vision-pro) its electric car project, known as "Project Titan." The company decided to shift its focus and resources toward building more AI tools instead.

The electric car project had been going on for almost a decade, with thousands of employees working on it. But Apple realized that competing in the car industry would be extremely hard and expensive, especially with strong players like Tesla already ahead.

What Happens Next?

Many of the people who were working on the car project will now join Apple’s AI teams. Apple is investing heavily in AI, trying to catch up with companies like OpenAI and Google. They plan to bring more AI features to iPhones, Macs, and other devices soon.

Why It Matters

Apple rarely cancels big projects like this, so it shows how serious they are about focusing on AI now. Some people are disappointed because they were excited to see an Apple car, but others are excited to see what new AI tools Apple will create instead.

Would you have wanted to drive an Apple car?

Being Polite To ChatGPT Costs OpenAI Tens of Millions

OpenAI CEO Sam Altman revealed (https://x.com/sama/status/1912646035979239430) on X that polite user prompts to ChatGPT cost the company tens of millions of dollars. "Well spent—you never know [what might happen]," he added, hinting at a future where rudeness to AI might backfire.

While Altman's tone was ironic and not meant to provide exact figures, his comment sparked a debate: should we waste resources being polite to a machine?

Where Do the Costs Come From?

Each prompt to a chatbot triggers a chain of computations: chips spinning up, cooling systems running, and other infrastructure working in data centers. Even a few "extra" tokens in a prompt increase server load and power consumption.

Researchers at Epoch AI estimated (https://epoch.ai/gradient-updates/how-much-energy-does-chatgpt-use) that a single GPT-4o query requires about 0.3 watt-hours of electricity—enough to boil a teaspoon of water. Words like "please" and "thank you" are a small part of a conversation. Still, across billions of interactions, they add up to megawatts of power—and serious operating costs.

Who Says "Thanks" to AI?

In the U.S., 67% of people who use AI are polite to it. (https://www.techradar.com/computing/artificial-intelligence/are-you-polite-to-chatgpt-heres-where-you-rank-among-ai-chatbot-users) Of those, 55% say it's simply "the right thing to do," and 12% say it's just in case AI or robots ever become conscious and remember who treated them with respect. Some Reddit and X users say it plainly feels wrong to be rude, even to an algorithm.

And maybe they're onto something: research shows (https://aclanthology.org/2024.sicon-1.2/) that politeness can improve AI responses. Curtis Beavers, a director on the design team for Microsoft Copilot, explains (https://www.microsoft.com/en-us/worklab/why-using-a-polite-tone-with-ai-matters) that LLMs mirror the user's tone, responding not only politely but also more professionally. We covered this in more detail here. (https://t.me/hiaimediaen/832)

So, are you polite to AI?

MLOps: The Secret Sauce of Real-World AI

You’ve built a cool AI model that can detect cats or predict stock prices. But… how do you actually use it in the real world without it breaking?

That’s where MLOps comes in!

What is MLOps?

MLOps = Machine Learning + DevOps

It’s how we:

Train and test models

Deploy them into apps

Monitor and retrain them when needed

Think of it as taking your AI model from your laptop to the real world—and keeping it smart and reliable.

Why MLOps Rocks 🤔

Without MLOps:

Models break when deployed

Data changes mess things up

Teams waste time doing things manually

With MLOps:

Models update automatically

Everything is versioned and tracked

Teams move faster and smarter

Key Tools (Quick Look)

Versioning: Git, MLflow

Pipelines: Airflow, Kubeflow

Deployment: Docker, FastAPI

Monitoring: Prometheus, Grafana

Final Tip

MLOps = Reliable AI.

It’s not just about building models—it’s about making sure they work in real life.

If you want your AI to be useful, MLOps is a must!

Image from site