As an AI professional aiming for a promotion, we should first develop skills in advanced machine learning techniques, deep learning frameworks, data analysis and visualization, project management, and effective communication to demonstrate our ability to lead AI initiatives and drive business impact.

Python is the most commonly used language in AI, so I would learn that language first if you are looking to go on the technical side, if you are looking to be more of an operational AI practitioner, I would look at AI from more of a broad scope and gain understanding of its impact at economic, social and environmental levels and tie those things back to your organization and product management. #ai #artificialintelligence #aieducation #ailelarning

I'm going to be brutally honest. In a world where generative AI exists, all technical skills are null and void because those are the first skills generative AI can and will replace. Only thing that matters is who you know and how good you are at selling the value of your opinion. Focus on networking, developing a point of view and forget programming languages.

While Python reigns supreme in AI, the real game-changer lies in mastering AI interactions over traditional coding. As AI begins to automate programming, the ability to craft effective prompts emerges as a pivotal skill. This shift doesn't diminish coding's value but highlights the importance of evolving with AI's capabilities. Focus on becoming an AI whisperer, blending coding with strategic AI guidance. The future is about directing AI, not just coding it! 🚀🧠

Math and stats remain crucial in AI, particularly for research and algorithm development. However, as AI advances, it increasingly automates these complex analyses. The takeaway? Cultivate a solid foundation to innovate and guide AI, but also adapt, focusing on leveraging AI's capabilities. This balance ensures you're not just a mathematician but a strategic AI navigator. 🧭🚀

Geometry for AI One subject in Math that does not get talked about a lot is Geometry. Geometry is my favorite!! Its applications in the AI sphere could include where computations need to happen in imaging say for example in geography, depth analysis, occlusion elimination etc etc #ai #artificialintelligence #geometry #aiimaging

Complex mathematical and statistical ideas like calculus, linear algebra, probability, and optimization form the foundation of artificial intelligence. These ideas ought to be well understood by you, as well as how they relate to AI issues. Additionally, you must to be able to analyze data, test hypotheses, and assess outcomes using mathematical and statistical techniques and tools. For instance, you should understand how to analyze data using regression, correlation, hypothesis testing, and descriptive and inferential statistics. For optimization, you should be familiar with gradient descent, convex optimization, and Lagrange multipliers.

Complex mathematical and statistical ideas like calculus, linear algebra, probability, and optimization form the foundation of artificial intelligence. These ideas should be thoroughly understood by you, as should how they relate to issues of AI. To assess findings, test theories, and analyze data, you must also be proficient in the application of statistical and mathematical tools and techniques. For instance, you should be able to analyze data using regression, correlation, inferential and descriptive statistics, and hypothesis testing. In the context of optimization, you ought to be familiar with gradient descent, convex optimization, and Lagrange multipliers.

Diving deep into machine learning (ML) and deep learning (DL) is essential for AI mastery. These fields are where the magic happens, from data analysis to predictive modeling. Familiarity with various ML/DL techniques and tools like TensorFlow or PyTorch not only equips you with the skills to build innovative solutions but also keeps you at the forefront of AI development. Remember, it's not just about knowing the tools but understanding how to apply them creatively to solve real-world problems. So, gear up to explore the vast landscapes of ML and DL, and let your curiosity lead the way to groundbreaking discoveries! 🚀🤖

Engineers provide labeled/unlabeled data for algorithm training since machine learning relies on this for predictions. For ex., an algorithm could predict a person's decision-making tendency based on other personality types, you could show the AI various mock/fake personality profiles & the algorithm learns patterns & makes appropriate predictions Deep learning works with unstructured data by pulling features direct from the data or images which may be pixelations or objects within images & then it puts a conclusion together based on hierarchical data which in simple terms hierarchy could mean IFTTT or cause/effect approaches. Engineers would use hyper-parameters to determine the pace and method in which the algorithm is trained. #ai

The two main areas of artificial intelligence (AI) that deal with developing algorithms and models that can learn from data and carry out operations like generation, regression, clustering, and classification are machine learning and deep learning. The primary categories and methods of deep learning and machine learning, including reinforcement learning, supervised, unsupervised, and neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks, should be familiar to you. Additionally, you should be able to create, train, and deploy your models using machine learning and deep learning frameworks and libraries like scikit-learn, TensorFlow, PyTorch, or Keras.

I think it is valuable for everyone to have a basic understanding of AI concepts. Particularly now as we are entering a golden age of AI. I expect that the future will see frameworks and solutions abstract away some of the need to learning libraries in detail with more focus on the AI lifecycle management processes and applying things like MLOps and LLMOps.

Working with AI requires more than technical skills, it calls for new perspectives on old patterns. E.g., the long-established input-processing-output principle. In LLMs, inputs based on mass data cause unpredictable side-effects. Processing based on statistical methods can hardly be debugged. Outputs are costly to explain and hallucinate. A negative impact to customer satisfaction and inappropriate for industries like manufacturing or healthcare. Hence, we need to develop & improve skills to evaluate in- & outbound data quality in domain-specific contexts, to understand and combine the strengths of relational DBs, semantic knowledge graphs, LLMs and embedding-based vector DBs. Essentials to successfully establish data driven value chains.

Navigating the data terrain is pivotal for AI success. Your emphasis on clean data and efficient training even with less data highlights a crucial skill: the ability to refine and manage data smartly. Knowing how to collect, clean, preprocess, and transform data forms the bedrock of AI's learning process. Mastery in data engineering and management ensures that the AI systems we build are not just powerful but also precise and resource-efficient. By focusing on these skills, you're essentially ensuring that your AI projects stand on solid ground. Dive deep into the world of data, for it's where the journey of AI begins and thrives! 🌐💾

Pruning data can indirectly contribute to better data management practices Basically it strips out weights and parameters which allows the data make decisions and / or predictions that are more accurate. Doing this could possibly or likely indirectly have positive results where the model produces better performance, more accurate predictions from reduced complexity and redundancies and inconsistencies in the data #ai #artificialintelligence #datamanagement

You gather and clean data, transform it into a usable format , and store it in databases. Then, you present your data beautifully.

Never underestimate of being able to communicate complex ideas in a simple and compelling way. Generally speaking the higher up the organisational chain, the more simple you need to keep messages due to information overload of execs. Those who can cut through to the key message with sound analysis will stand out from the crowd.

In addition to being a technical discipline, artificial intelligence is also a social and multidisciplinary one. It is essential that you have the capacity to collaborate and communicate with AI experts as well as stakeholders from various domains and backgrounds. It should be possible for you to effectively and convincingly communicate your concepts, techniques, and conclusions using appropriate terminology and formats, like as reports, presentations, or dashboards. You should be able to listen and reply to inquiries, remarks, and ideas in addition to being able to collaborate with others and offer your expertise.

AI solutions depend on many technologies (data, cloud, ML, etc.) & practices across silos (IT, sales, Quality control, supply chain, etc.). If you want to make a mark, you should be able to communicate effectively with all these silos. Interpersonal skills are the foundation. But also you should learn the basic challenges & lingo of each silo you want to work with. That seems harder than it really is, especially if you have curiosity & a bit of a generalist in you. It's very easy these days to learn the basics enough to communicate intelligently with people in various practices. Hint: use chatGPT! The idea is NOT to become an expert, but to ask the right questions & to think integratively.

Communication and collaboration are essential skills in AI development, involving: 1. **Communication**: Effectively conveying technical concepts and insights to non-technical stakeholders, fostering understanding and alignment. 2. **Collaboration**: Working effectively with interdisciplinary teams, including data scientists, engineers, and domain experts, to leverage diverse perspectives and skills in AI projects. These skills facilitate successful project outcomes and promote a culture of innovation and teamwork within AI initiatives.

Don't underestimate the importance of ethics and responsibility. Organisations should have model governance in place to assess compliance to standards and evidence that the models have been trained appropriately, without bias and with appropriate data controls. In some ways this can be more challenging than engineering an innovative AI solution!

Keep it simple. Do not develop intelligence that does to others what you don't want others to do to you. Be especially vigilant here, because AI can be scaled practically infinitely. Damage that might be negligible if done human to human can be severe if done AI to human. Remember there are multiple AI parties here: developer, buyer, operator, end user. The weakest party is the end user. Keep that in mind. AI ethics is complicated. I can't cram it here and there are no shortcuts. If you are responsible, you will educate yourself. Be a good citizen and a good human.

What is a responsible AI? I would say it's the one that combines ethics and explainability. Here are the 7 pillars of a responsible AI. Fairness, explainability, accountability, transparency, Reliability, Privacy, and security By building AI on these pillars, we can create technology that not only mimics human intelligence but also respects human values and rights.

Amen Philip! We should all be asking about the ethics of what is being created. I’m take a course called AI Ethics and Board Oversight and it’s a real eye opener. I saw a couple stories about AI selling a $80k truck for a dollar, giving a free airline ticket to a customer and a chess playing robot breaking a 7 year olds finger during a game when the child went to move a board piece. Crazy.

Navigating the ethical landscape of AI, from ensuring fairness and privacy to upholding security and accountability, is a critical competency. Awareness and adherence to ethical guidelines and best practices ensure the development of AI solutions that are not only innovative but also responsible and trustworthy.

Learn how to fully engage with conversational AI and write effective prompts. One of the biggest opportunities for right now is simply learning how to use the tools, showing leaders the art of what’s possible, and coming up with innovative ways to use this technology. Be passionate. Be an enthusiast. Be AI.

AI might seem magical, but I assure you, AI practitioners are still subject to the usual laws of business & common sense. In fact, more so. Advancing (usually) requires success. So, make sure you are in AI projects that are likely to succeed. Most do not, because people get caught up in magical thinking. AI solutions still need to create specific value for a specific customer set. Nobody will pay real money for solutions that do not create value. Make sure the value numbers "add up". Make sure you do not make magical assumptions. Learn that part of the equation & teach it to the others. Pay attention to the Total Cost of Ownership.

I am unsure if I got the aim of the article right. If you want to be promoted from a junior to a senior AI developer, I would agree with the statements. If you however want to be come an AI leader, you should have the ability to have a good understanding of the operational business of your company to become a business partner and to suggest other business areas within your company where e.g. existing AI cases can be adopted to

Read and evaluate papers across scientific societies. Then also contribute to novel research across todays genAI challenges. Ultimately getting ahead/promotes in AI is a function of how much impact you’ve created, whether that’s transforming lives, business or solving previously unsolved problems.

Beyond technical skills and ethical standards, staying curious and embracing continuous learning are key. The AI field is rapidly evolving, with new challenges and technologies emerging regularly. Sharing personal experiences, such as overcoming a particularly challenging project or how a unique approach to an AI problem led to unexpected success, can provide valuable insights and inspire others. By focusing on these core areas, AI professionals can not only prepare themselves for advancement but also contribute positively to the field, driving forward innovations that are both impactful and ethically sound.