Generative adversarial networks (GANs) have enabled the AI industry to take huge leaps toward creativity. However GANs are opaque, which means there’s very little visibility or control on how they work. As a result, engineers find it hard to troubleshoot them, and users find it hard to trust them. To overcome these limitations, researchers have developed a technique called “GAN Dissection” that helps explore the inner workings of GANs and better understand the reasoning that results in their output. The work is one of several efforts collectively explainable AI, that can interpret AI decisions
AI is not something to be feared. It’s not an impending robot revolution. It’s not an economic tidal wave rushing to wipe out jobs and create large-scale unemployment. What it is is another technological disruption, and just like any other technological disruption of the past, it is bound to cause some shock waves. These changes, however, are not something to be feared, but embraced. We need to stop treating AI as the villain in some low-budget sci-fi horror film.
Practical studies have proven Evolutionary Deep Learning applications to be a useful method for advancing the state of the art. Nevertheless, lots of limitations are still present in employed methods, just like the use of predefined building blocks for Neural Architecture Search and non-crossover nor mutation used in Evolutionary Deep Learning. Also, it is noticeable that Evolutionary Algorithms are seen as black-box optimization methods and thus they provide little understanding of why the performance is high. Further research will decide the future of Evolutionary Algorithms in Deep Learning.
The vast majority of data science job descriptions do not convey the actual requirements of the position they’re advertising. When senior data scientists are called upon to recruit, their first move is often to throw away the job posting altogether. A misleading job description means that recruiters get a ton of irrelevant applications, and that candidates waste a ton of time applying to irrelevant positions. But there’s another problem: job descriptions are the training labels that any good aspiring data scientist will use to prioritize their personal and technical skills development.
Competence refers to having sufficient skill, knowledge, and experience to perform the job, i.e. being properly qualified. The common set of skills that are required to be a Chief Data Executive include knowledge of the business and mission, knowledge of computer science, data science, or both, and knowledge of product definition and delivery. A competent Chief Data Executive is a rare mix of technical guru, businessperson, marketer, and adept executive leader — someone able to communicate in all spheres and that can easily translate between each.
Nuances exist within the different Data Science Career Levels. For instance, some rather look for strong engineering than math skills in Junior Data Scientists. Some Senior Data Scientists might discover their passion for building scalable data pipelines and transition to a Data Engineering role. Some Principal Data Scientists prefer to develop technical expertise while others rejoice in focusing on business skills. Whatever career path you take, developing your skills around the three main areas of Data Science expertise will get you far.
It is essential for a machine learning engineer to have a good understanding of Vectors, one of the most crucial concepts within Machine Learning because many bugs are due to having matrix /vector dimensions that don’t fit properly. A common problem in machine learning is that a model is not really accepting the data and therefore keeps throwing errors. Often the solution lies in vectorizing the data which means nothing more than reshaping the data into the required dimensions.
These days, ‘quantum’ is a byword for where we are going, although the technology is still in its genesis. To understand quantum computers and what — if our minds and design applications get to grips with them — they can do for us is mindblowing. Quantum theory is for many a subject so difficult to understand, so sheathed in intellectual rhetoric that it frightens the life out of many if only because of its sheer incomprehensibility. A quantum race could very much be on the cards.
With the regulators taking a much closer look into blockchain technology, the trend will probably be less hype, fewer scams, and a lot more substance with carefully planned and executed projects in this space. The convergence between blockchain and the internet of things (IoT) is likely to continue as organizations start realizing and understanding the security features offered by this technology. Continuation of the period of relative stability is thus expected.
Compared to financial advisors, robo-advisors are inexpensive and more accurate in allocating assets, estate planning, and overall financial advice. Robo-advisors help private investors in wealth management with the help of predefined algorithms and trends in the financial market. The utilization of robo-advisors in fintech is not a new phenomenon. Wealth managers have been using robo-advisors behind-the-scenes to gain additional information before offering their final recommendation to clients. As robo-advisors became more advanced, wealth managers were able to focus more on building client relationships and save time spent on data entry and investment management.
Welcome to the world of tensors in AI. It is now time to get used to the curse of dimensionality. It is also an industry standard practice to flatten tensors all the way to matrices to leverage the highly optimized libraries for matrix multiplication (MM) or MM accelerators (MMAs), even though tensors are considered to be the most fundamental data type in all major AI frameworks. Matrices are generally considered to be special cases of tensors by the AI community.
Your enterprise must take to ensure security, governance, and compliance over the content and communications that take place through your enterprise collaboration tools. The good news is that this work takes place at one level, and what comes from that work can become a set of standard policies to govern team-level collaboration sites. It’s essential to create corporate policies and training for teams that may be opening their collaboration sites to external parties.
How do you know when you have achieved DevOps? and you are likely to get different answers. A practical answer to how do you know when you have DevOps depends on having a definition. While defining DevOps itself has been elusive, an enterprise definition is needed for alignment and progress measurement. Considering that DevOps needs continuous flow to accomplish business goals, it can be said you have DevOps when you have implemented continuous flow for at least one model application.
Blockchain can solve a lot of issues continually faced by banks and financial organizations nowadays. It could bring changes to everything from payments to online appointment scheduling software. It provides a high level of security in storing and transforming data at low costs through an open and transparent network infrastructure. The traditionally centralized, conservative, and restricted banking sector has now started using blockchain technology more than any other institutions. The change of the bank's clearinghouse or centralized ledger to blockchain's distributed ledger could redefine the banking industry.
Leadership teams tend to have innate biases for certain asset types, and that these preferences drove business model. Like a good driver, a leader needs to know when to speed up to catch the competition, when to shift investment into the right kinds of capital, and when to refuel with new skills, mental models and board members. Just as the human genome offers the prospect of personalized medicine, the value genome offers the prospect of tailored capital editing—refocusing companies on high-value, scalable assets.
What exactly constitutes an AI Strategy? What are the differences in creating an AI Strategy for startups vs corporations? While much is known about creating a business strategy, creating an AI Strategy is new territory. How do you approach creating your AI Strategy? In this article, you will learn how to approach creating an AI Strategy. Think of AI’s core components when creating your AI Strategy. We are looking forward to a world that embraces the decade of AI implementation.
The goal of supervised learning is to extract all the juice from the relevant features and to do that, we generally have to enrich and transform features in order to make it easier for the algorithm to see how the target variable depends on given data. One type of features that do not easily give away the information they contain are categorical features. They keep on hiding the information until we transform them smartly. In this particular post, I am focussing on one particular categorical encoding technique called target encoding.
When creating the product team, ensure you listen to feedback from the individuals likely to be impacted, don't just do a paper exercise of moving people into new organisational structures. The teams on the ground know what skills and resources they need in their product teams, they know the issues they face and will have good ideas on how to address them, the key aim is to maximise the flow of valuable work into the team.
Natural Language Generation capabilities have become the de facto option as analytical platforms try to democratize data analytics and help anyone understand their data. Close to human narratives automatically explain insights that otherwise could be lost in tables, charts, and graphs via natural language and act as a companion throughout the data discovery process. Besides, NLG coupled with NLP are the core of chatbots and other automated chats and assistants that provide us with everyday support.
While DevOps offers immense value for software deployment, the adherence to best practices is essential to reduce risk and assure security. Each organization is different and has different security postures. This blog enumerates best practices for security across nine pillars of DevOps: Leadership, Collaborative Culture, Design for DevOps, Continuous Integration, Continuous Testing, Continuous Monitoring, Elastic Infrastructure, Continuous Delivery/Deployment and Continuous Security. Examples of best practices for each pillar are listed. These practices can be used to assess an organization’s maturity within the journey to Continuous Security, often referred to as DevSecOps.
All around us and most of the time without us even realizing it computer vision (CV) is being used to enhance our lives. New levels of development have been achieved. The sky’s the limit for the future of CV with the advent of better, more sophisticated processors which not only the large tech firms have access to anymore. The wider availability of software in deep learning has democratized this area of the tech industry.
The rise of AI taught us that humans are nothing but another input and output. Words and emotions are the programming language and the machines can program us with ease. We once imagined we were the only masters of our destiny and the sole captain of our ships. Our minds were made up by our own free will. But we know it’s not true now. As we untangled the inner mysteries of our minds’ complex electrochemical field we realized we were just another kind of artificial intelligence, one evolved through the great genetic algorithms of the Earth’s biome.
Multi-model databases (MMDB) have rapidly gained importance in the market. However, is MMDB the right choice for your project or enterprise? While there are plenty of advantages, multi-model is not the ultimate solution for every situation. It is not a way to force developers to use a variety of data models, nor can one layered or native multi-model database integrate every data model efficiently. It’s more about enabling developers to leverage the advantages of different models for different aspects of their applications.