We as human may have often wondered if the intelligence of human can be copied and machines can work the same way as us. While it is still a distant dream but we are not very far away. In the path to artificial intelligence lets have an overview of what it really means and how data science is helping us achieve it. A) Artificial Intelligence: It is an important science that actually helps in daily activities nowadays. The end goal of any machine learning or deep learning algorithm is achieving artificial intelligence.
Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.
The emergence of Industrial Internet of Things (IIoT) leads in developing automated environments, such as smart factories, smart airports and smart healthcare systems. AI applications enable the automation and data analytics across industrial technologies, including the Internet of Things (IoT), cloud and edge, and fog computing paradigms. Existing Artificial Intelligence (AI), especially Deep Learning (DL) models, still suffer from designing a generalized architecture that reveals semantics and contexts of models, considering Human-in-the-loop (HITL). Adversarial Machine Learning (AML) models have been widely utilized to fool DL applications using malicious actors. This makes a great interest to establish white-box models, rather than black-box ones, to determine their trustworthiness and reliability in business problems in IIoT networks.
Modern AI has produced models that exceed human performance across countless tasks. Now, an international research team is suggesting AI might become even more efficient and reliable if it learns to think more like worms. In a paper recently published in Nature Machine Intelligence journal, the team from MIT CSAIL, TU Wien in Vienna, and IST Austria proposes an AI system that mimics biological models. The system was developed based on the brains of tiny animals such as threadworms and is able to control a vehicle using just a small number of artificial neurons. The researchers say the system has decisive advantages over other deep learning models because it copes much better with noisy input, and, because of its simplicity, its operations can be explained in detail -- alleviating the "black box" concerns affecting today's deep AI models. Explains TU Wien Cyber-Physical Systems head Professor Radu Grosu in a project press release: "For years, we have been investigating what we can learn from nature to improve deep learning.
To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue. Since data science is broad, with methods drawing from computer science, statistics, and other disciplines, and with applications appearing in all sectors, these challenge areas speak to the breadth of issues spanning science, technology, and society. We preface our enumeration with meta-questions about whether data science is a discipline. We then describe each of the 10 challenge areas. The goal of this article is to start a discussion on what could constitute a basis for a research agenda in data science, while recognizing that the field of data science is still evolving. Although data science builds on knowledge from computer science, engineering, mathematics, statistics, and other disciplines, data science is a unique field with many mysteries to unlock: fundamental scientific questions and pressing problems of societal importance.
Deep Learning is changing the way we look at technologies. There is a lot of excitement around Artificial Intelligence (AI) along with its branches namely Machine Learning (ML) and Deep Learning at the moment. With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Artificial intelligence is finally getting smart. It's predicted that many deep learning applications will affect your life in the near future.
Unlike any other time, the past decade has seen unprecedented development in the field of Artificial Intelligence (AI). There are a lot of talks on machine learning doing things humans currently do in our workplace. Deep learning is leading in some of the fronts of machine learning making practical changes. Deep learning is an artificial intelligence function that imitates the working of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural network (ANN).
MIT CSAIL project shows that neural nets contain "subnetworks" 10x smaller that can just learn just as well - and often faster These days, nearly all AI-based products in our lives rely on "deep neural networks" that automatically learn to process labeled data. For most organizations and individuals, though, deep learning is tough to break into. To learn well, neural networks normally have to be quite large and need massive datasets. This training process usually requires multiple days of training and expensive graphics processing units (GPUs) - and sometimes even custom-designed hardware. But what if they don't actually have to be all that big after all?
It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".