The #AI value chain, 1) AI chip and hardware makers who are looking to power all the AI applications that will be woven into the fabric of organisations big and small globally 2) The #cloud platform and infrastructure providers who will host the AI applications 3) The AI #algorithms and cognitive services building block makers who provide the vision recognition, speech and #deeplearning predictive models to power AI applications 4) Enterprise solution providers whose software is used in customer, HR, and asset management and planning applications 5) Industry vertical solution providers who are looking to use AI to power companies across sectors such as healthcare to finance 6) Corporate takers of AI who are looking to increase revenues, drive efficiencies and deepen their insights The today's AI is presented by what the BigTech and global social media platforms are pushing, it's Narrow /Weak AI /ML /DL, as "Cloud DL/AI Platforms". But this #Machinelearning algorithms are designed to optimize for a cost/loss function, having no intelligence, understanding or reasoning. So it is, Most curve-fitting AI tools available today sold as focused on predicting, identifying, or classifying things, a rote "learning from data/experience".
Companies today are leveraging more and more of user data to build models that improve their products and user experience. Companies are looking to measure user sentiments to develop products as per their need. However, this predictive capability using data can be harmful to individuals who wish to protect their privacy. Building data models using sensitive personal data can undermine the privacy of users and can also cause damage to a person if the data gets leaked or misused. A simple solution that companies have employed for years is data anonymisation by removing personally identifiable information in datasets.
Software Engineering, as a discipline, has matured over the past 5 decades. The modern world heavily depends on it, so the increased maturity of Software Engineering was an eventuality. Practices like testing and reliable technologies help make Software Engineering reliable enough to build industries upon. Meanwhile, Machine Learning (ML) has also grown over the past 2 decades. ML is used more and more for research, experimentation and production workloads. But ML Engineering, as a discipline, has not widely matured as much as its Software Engineering ancestor. Can we take what we have learned and help the nascent field of applied ML evolve into ML Engineering the way Programming evolved into Software Engineering? In this article we will give a whirlwind tour of Sibyl and TensorFlow Extended (TFX), two successive end-to-end (E2E) ML platforms at Alphabet. We will share the lessons learned from over a decade of applied ML built on these platforms, explain both their similarities and their differences, and expand on the shifts (both mental and technical) that helped us on our journey.
Offered through a collaboration with Microsoft, this microcredential will teach you the fundamentals of AI and provide you with the skills to design and build an AI solution using Microsoft Azure. We will prepare you for the Microsoft Azure Fundamentals (AZ-900) and Microsoft Azure AI Engineer Associate (AI-100) certification; the cost of this microcredential includes vouchers for those exams. Artificial intelligence is one of the key drivers of the Fourth Industrial Revolution. Accordingly, artificial intelligence skills are frequently listed among the most in-demand workplace skills in the current and future job market, as organisations seek to harness AI to revolutionise their operations. While in-demand tech skills are changing, employers are faced with a shortfall of qualified candidates.
Artificial intelligence (A.I.) is expected to significantly influence the practice of medicine and the delivery of healthcare in the near future. While there are only a handful of practical examples for its medical use with enough evidence, hype and attention around the topic are significant. There are so many papers, conference talks, misleading news headlines and study interpretations that a short and visual guide any medical professional can refer back to in their professional life might be useful. For this, it is critical that physicians understand the basics of the technology so they can see beyond the hype, evaluate A.I.-based studies and clinical validation; as well as acknowledge the limitations and opportunities of A.I. This paper aims to serve as a short, visual and digestible repository of information and details every physician might need to know in the age of A.I. We describe the simple definition of A.I., its levels, its methods, the differences between the methods with medical examples, the potential benefits, dangers, challenges of A.I., as well as attempt to provide a futuristic vision about using it in an everyday medical practice.
Welcome to part 4 of my AI and GeoAI Series that will cover the more technical aspects of GeoAI and ArcGIS. Previously, part 1 of this series covered the Future Impacts of AI on Mapping and Modernization which introduced the concept of GeoAI and why you should care about having an AI as a future coworker. Part 2 of the series, GIS, Artificial Intelligence, and Automation in the Workplace covered specific geospatial professions that will be drastically effected by introduction of GeoAI technology in the workplace. Part 3 addressed Teaming with the Machine - AI in the workplace the emergence of the new geospatial working relationship between information, humans, and artificial intelligence to be successful in an organizations mission. For part 4, we will address 3 specific GeoAI areas in ArcGIS that will help you with your journey to developing your Deep Learning workflows.
Facebook's artificial intelligence researchers have a plan to make algorithms smarter by exposing them to human cunning. They want your help to supply the trickery. Thursday, Facebook's AI lab launched a project called Dynabench that creates a kind of gladiatorial arena in which humans try to trip up AI systems. Challenges include crafting sentences that cause a sentiment-scoring system to misfire, reading a comment as negative when it is actually positive, for example. Another involves tricking a hate speech filter--a potential draw for teens and trolls.
Benchmarking is a crucial step in developing ever more sophisticated artificial intelligence. It provides a helpful abstraction of the AI's capabilities and allows researchers a firm sense of how well the system is performing on specific tasks. But they are not without their drawbacks. Once an algorithm masters the static dataset from a given benchmark, researchers have to undertake the time-consuming process of developing a new one to further improve the AI. As AIs have improved over time, researchers have had to build new benchmarks with increasing frequency.
According to 2015 APQC, 62% of accounts payable costs come from labor - and that figure doesn't account for the opportunity cost of wasting time that could be better spent on innovation and strategic thinking. At SAP Concur, we have been using Machine Learning (ML) for several years to do things for our customers that could not be done any other way. With SAP Leonardo, we continue investing in the future of ML and AI with a set of innovative services that make everything from travel booking to expense auditing smarter, more automated and easier for your employees. Download the white paper now, and learn more at www.concur.com.sg