Here's how you can navigate the future of algorithms with new technologies and tools.

I’ve found approaching AI integration/strategy via the framework of data science & data mining to be very useful, it improves how much LLM based AI systems can uncover in any kind of domain specific dataset.

Artificial Intelligence (AI) is transforming the development and deployment of algorithms, offering unprecedented capabilities and efficiencies. By incorporating AI into my algorithmic toolkit, I can harness the power of machine learning models to analyze large datasets and generate highly accurate predictions. AI also facilitates the continuous improvement of algorithms by learning from new data, leading to better outcomes over time. To remain at the forefront of this technological wave, I am committed to familiarizing myself with AI frameworks and exploring their potential to enhance my algorithms in areas such as image recognition, natural language processing, and predictive analytics.

LLMs for Coding: LLMs like GPT-4 are revolutionizing coding by generating code snippets, debugging, and suggesting optimizations. Incorporating LLMs can significantly enhance coding efficiency. Tasks that previously required a team of 10 engineers working for months can now be accomplished by a single engineer in just a few days with the help of LLMs. LLMs provide two core benefits: Ready-to-Use Solutions: LLMs can generate almost-ready-to-use code that engineers can review, verify, and edit, saving time/costs. Multi-Language Support: LLMs enable engineers to quickly write and understand code in multiple programming languages, broadening their capabilities and speeding up development.

On thing that i've found helpful is using code assistants in my daily coding routine. They help developers not only to write less boring codes but also enable them to solve more challenging tasks with creative solutions.

I think AI is like a two-sided sword. While it can be used as a very powerful tool to improve the solutions results significantly, on the other hand if not used properly, it could only be a waste of resources including time and money. Unfortunately nowadays, many companies are pretending applying AI for showing they’re walking on the cutting edge, however using AI for their solutions does not make sense in the first place. Maybe in the first glance that would seem very appealing, but over time the house of cards they created would be vanished by the weakest blow of a new requirement for example, and then it would be very late to make a proper decision.

The Big Data course at ETH Zurich is excellent for those looking to stay up to date in this rapidly evolving field. The course covers a wide range of fundamental topics, from the motivations of Big Data to more advanced subjects like massive parallel processing with MapReduce and generic dataflow processing with Spark. It brushes up SQL, data modeling, and querying denormalized data. Practical lessons on resource management, object storage, and distributed file systems ensure that participants can handle real-world data challenges effectively. Blending theoretical knowledge with practical exercises, this course is an excellent resource for both new learners and seasoned professionals aiming to refine their mastery of Big Data technologies.

These days, any company that interacts with users can use that data to develop their business by properly storing data and relying on artificial intelligence and big data solutions.

Quantum technologies are by far one of the most interesting hot topics of the decade. With so many aspects to their development (hardware, software, commercialisation,…) and due to their closeness with various pure scientific topics, they have been for me an incredible space to develop a career. Although not for the light hearted, if you are a scientist (or an aspiring STEM student) looking for your niche, do explore them!

Quantum computing is still an area of open research which needs massive amounts of funds to capitalize upon, bcoz in theory Quantum computers are indeed very powerful and open new dimensions of computing but the biggest challenge lies in terms of accessibility. Due to particle physics of the materials involved in building of a QC, it's very difficult to make it available to the masses at reasonable prices. Moreover it hosts a variety of challenges too like read/write operations in QC is not easy, bits are not processed like a traditional computer(we have qubits here) and it's architecture is also quite different. So QCs won't replace the existing computers but they will complement it, as they will be used for very specific purposes

I think cloud based solutions can be useful in various cases by saving money and time. However, the before starting using cloud one should be very aware of all aspects of. Most importantly, in my opinion, data privacy concerns. Also using cloud for every single problem, could lead to waste of money, as there might be a very less complex and cheaper solution. However, there are cases in which cloud would a real game changer. I find it very essential to research deeply analysing the problem, to reach to the best-fitting solution, on base of the performance per cost as an example metric.

Cloud integration stands as a powerful enabler for businesses seeking fast availability and cost-cutting advantages. By embracing cloud-based solutions, organizations can accelerate their operations, enhance collaboration, and achieve greater financial efficiency. As the business landscape continues to evolve, cloud integration will undoubtedly remain a cornerstone of modern enterprise strategies.

A revolutionary AI framework integrates the principles of the time-tested Rotary 4-Way Test to ensure strong ethical standards in artificial intelligence. EthosNet is designed to enhance the capabilities of AI systems while maintaining a steadfast commitment to ethical integrity, devoid of political or religious influence. By integrating the Rotary 4-Way Test, this ensures that AI systems are truthful, fair, build goodwill, and provide benefits to all. This ethical foundation, combined with advanced neural network architecture and quantum-inspired technologies, makes it's code the ideal choice for the future of AI promoting trust, transparency, and positive societal impact.

I think it’s very important that the AI algorithms would be developed in a way considering ethical values, like being unbiased and fair. However, in my opinion, the algorithm embodies the reflection of the society. So at the very first place, the social values should be aligned with these concepts, which is achieved by raising self-awareness, and education. Only then, the data in which they are trained would be unbiased, so leading to the preferred results.

Staying updated in the dynamic field of algorithms is essential. Commit to lifelong learning by participating in online forums, attending workshops, and reading the latest research. Continuously update your skills to adapt to changes, ensuring you remain a valuable asset. Embrace new techniques and tools to navigate the future successfully and stay at the forefront of technological advancements.

Artificial intelligence models are becoming more assertive, even with a reduction in the number of parameters and the implementation of advanced quantization techniques from 32 to 16 and 8 bits. These innovations enable the models to maintain high accuracy while significantly reducing the demand for computational resources, making them more efficient and accessible for use across various platforms. Taking the Gemma 2 as an example, which has 27 billion parameters, there is a surprising level of assertiveness considering its size. This model demonstrates how it is possible to achieve exceptional results in terms of precision and performance, even with fewer parameters and more compact quantization.

Being dynamic is one of the natures of computer science. As a developer who is involved with this dynamic world we need to update ourself. It is not limited to our fulltime job, we need to study and update our knowledge even outside of the job.

We hold algorithm and data structures meetings every Sunday, where we solve algorithmic challenges with JavaScript and explain the solution to other developers.