If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
PyTorch has sort of became one of the de facto standards for creating Neural Networks now, and I love its interface. Yet, it is somehow a little difficult for beginners to get a hold of. I remember picking PyTorch up only after some extensive experimentation a couple of years back. To tell you the truth, it took me a lot of time to pick it up but am I glad that I moved from Keras to PyTorch. With its high customizability and pythonic syntax, PyTorch is just a joy to work with, and I would recommend it to anyone who wants to do some heavy lifting with Deep Learning.
Before you start using'low code' or'drag & drop' data science tools, please learn the fundamentals. Why aspire to be'Citizen Data Scientist' when you can truly become a'Data Scientist.' Don't get swayed by the fancy titles like'Citizen Data Scientist.' It is funny that so much hard selling is happening in data science. I mean, just because we know how to use a thermometer or operate BP machine, should we start calling ourselves'Citizen Doctor'?
The power of wildfires to devastating has been tragically apparent across the world. Depending on terrain, they can stretch double in size every 10 minutes and every time that a fire burns makes it challenging to contain. With early detection and instant response, a fire may be quickly put out. In contrast, a fire can wipe out a massive amount of forest, killing and destroying as fast as it spreads. It is the job of a commercial agriculture technology company in Brazil, Sintecsys to surveil 8.7 million acres of forest and agricultural land around four biomes, including Amazon forest. Its system works around the clock to recognise fire and collect images from 360-degree cameras installed on towers distributed throughout the land.
Livongo is the leading Applied Health Signals company, partnering with over 800 clients to serve more than 250,000 members. One of Livongo's unique strengths is the suite of tools that enable our members to monitor not only their behaviors, but also their bodies -- for example, their blood glucose, blood pressure, and weight. These data streams provide vital clues into where each member is on their journey towards living a healthier and happier life -- and how Livongo can best provide support and guidance in an adaptive, just-in-time fashion. This role is focused specifically on furthering Livongo's ability to do more with our devices and their data streams, including: The Transformative Name in Healthcare: The transformative industry forces in Community, Content and Commerce are now household names. As Amazon is to Commerce, Livongo is to Healthcare.
Over the last few years, business leaders have invested millions into setting up AI/data science teams to gain a competitive advantage. Some AI initiatives have resulted in measurable benefits, but many haven't. Initial exuberance among business leaders has given way to skepticism. In our experience collaborating with Fortune 500 enterprises at TheMathCompany, we've observed that despite having sophisticated AI tools and star-studded data science teams on their side, weak links in strategic and operational aspects have deprived organizations of deriving meaningful value from AI. Based on our work across industry verticals helping organizations go through analytical transformations, I recommend five best practices to ensure tangible and sustainable value from AI.
Social Media and information sharing is something every internet user will know about. The presence and popularity of Twitter, LinkedIn, and many other platforms have made it convenient to spread knowledge all around the globe in a couple of clicks. It is because of the extensive usage of these networking sites by various Thought leaders, achievers, and change-makers that Data Science and AI knowledge has spread across the globe. IPFC online has recently come up with a list of Top 50 Digital influencers to follow out of which we are going to talk about the ones concerned with Machine Learning and AI. Additionally, we have provided some more influencers worth following.
There are three different approaches to machine learning, depending on the data you have. You can go with supervised learning, semi-supervised learning, or unsupervised learning. In supervised learning you have labeled data, so you have outputs that you know for sure are the correct values for your inputs. That's like knowing car prices based on features like make, model, style, drivetrain, and other attributes. With semi-supervised learning, you have a large data set where some of the data is labeled but most of it isn't. This covers a large amount of real world data because it can be expensive to get an expert to label every data point.
This comes out as a personal observation, but I'm sure that many of you will share the same feeling upon reading this post. I'm a data scientist, and I like my job because I think it covers various interdependent domains that make it rich and stimulating. However, I sometimes have to deal with people who don't exactly understand this role in the organization nor the field in general. This, quite frankly, is what makes things a little bit frustrating for me and also for a lot of people I know. Before you keep reading, I should mention that I don't aim to discourage anyone from aspiring to this role.
To learn the best, you must learn from the finest. Geoffrey Hilton is called the Godfather of Deep Learning in the field of data science. Mr. Hinton is best known for his work on neural networks and artificial intelligence. A Ph.D. in artificial intelligence, he is accredited for his exemplary work on neural nets. The co-founder of the term, "Data Science", Jeff Hammerbacher developed methods and techniques for capturing, storing and analysing a large amount of data.
Data science has made key contributions in the battle against COVID-19, from tracking cases and deaths to understanding how populations move during travel restrictions to vaccine design. The Harvard Data Science Initiative is working to support faculty members, students, and fellows in designing and applying the tools of statistics and computer science and creating a community to foster the flow of ideas. The year-old Harvard Data Science Review published a special issue online this summer dedicated to COVID-19 that will be updated with the latest findings, with a goal of fostering innovation and keeping the conversation going about how data science can help meet the COVID-19 challenge. The Gazette spoke with Francesca Dominici, Clarence James Gamble Professor of Biostatistics, Population and Data Science at the Harvard T.H. Chan School of Public Health and co-director of the initiative, and Xiao-Li Meng, the review's editor in chief and the Whipple V.N. Jones Professor of Statistics in the Faculty of Arts and Sciences, about how data science can be used to meet today's challenges, and in turn, challenges facing the field. GAZETTE: How is data science important to our understanding of and response to COVID-19? DOMINICI: Data science is on the front page of The New York Times probably every single day.