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) …
Women in Robotics is a grassroots community involving women from across the globe. Our mission is supporting women working in robotics and women who would like to work in robotics. We formed an official 501c3 non-profit organization in 2020 headquartered in Oakland California. We'd like to introduce our 2021 Board of Directors: Andra Keay founded Women in Robotics originally under the umbrella of Silicon Valley Robotics, the non-profit industry group supporting innovation and commercialization of robotics technologies. Andra's background is in human-robot interaction and communication theory.
Local businesses close as more National Guard troops deploy in the nation's capital. Selfie-snapping Capitol rioters left investigators a treasure trove of evidence -- at least 140,000 pictures and videos taken during the deadly Jan. 6 siege, according to federal prosecutors. The mass of digital evidence from media reports, live-streams and social media posts has been crucial to the FBI, which by Friday had identified more than 275 suspects, with close to 100 charged, officials said. Investigators have been working with social media and phone companies to help ID suspects -- as well as using advanced facial recognition technology, according to Bloomberg News. FILE: Rioters try to break through a police barrier at the Capitol in Washington.
With all the hype over Artificial Intelligence, there is additionally a lot of disturbing buzz about the negative results of AI. More than one-quarter (27%) of all employees state they are stressed that the work they have now will be disposed of within the next five years because of new innovation, robots or artificial intelligence, as indicated by the quarterly CNBC/SurveyMonkey Workplace Happiness review. In certain industries where technology already has played a profoundly disruptive role, employees fear of automation likewise run higher than the normal: Workers in automotives, business logistics and support, marketing and advertising, and retail are proportionately more stressed over new technology replacing their jobs than those in different industries. The dread stems from the fact that the business is already witnessing it. Self-driving trucks already are compromising the jobs of truck drivers, and it is causing a huge frenzy in this job line.
It is been so long since Harvard Business Review declared data science to be the sexiest job in 2012. Unfortunately, if we look back at how data scientist role is performing in the technology sector, it is more like the profession is slowly dying. Experts too think that the world is overrating data science professions throwing data at off-the-shelf algorithms. If we consider the'best jobs' ranking from 2017 to 2019, we see the data scientist role being dramatically losing its place. Data science played similar to'business analyst' position in the 2010s.
It could be argued artificial intelligence (AI) is already the indispensable tool of the 21st century. From helping doctors diagnose and treat patients to rapidly advancing new drug discoveries, it's our trusted partner in so many ways. Now it has found its way into the once exclusively-human domain of love and relationships. With AI-systems as matchmakers, in the coming decades it may become common to date a personalised avatar. This was explored in the 2014 movie "Her", in which a writer living in near-future Los Angeles develops affection for an AI system. The sci-fi film won an Academy Award for depicting what seemed like a highly unconventional love story.
Artificial intelligence seems to have transformed almost all the sectors across the world. On that note, healthcare sector has seen immense transformations over the years and the extent to which life has become convenient cannot be merely put into words. With 2020 being a year full of challenges especially on the healthcare front, not praising the healthcare sector for how it stood as a pillar is just not justified. Not surprising though, this sector has a plethora of opportunities that leverage technology to be deployed, hence paving way for Artificial Intelligence to explore a lot more areas. Having said that, AI has led to a lot of developments in the healthcare sector, making life simpler like never before.
Social network analysis is the process of investigating social structures through the use of networks and graph theory. This article introduces data scientists to the theory of social networks, with a short introduction to graph theory and information spread. It dives into Python code with NetworkX constructing and implying social networks from real datasets. We'll start with a brief intro in network's basic components: nodes and edges. Nodes (A,B,C,D,E in the example) are usually representing entities in the network, and can hold self-properties (such as weight, size, position and any other attribute) and network-based properties (such as Degree- number of neighbours or Cluster- a connected component the node belongs to etc.).
Machine Learning (ML) is a technique that uses algorithms to learn from the data without being programmed explicitly. Due to the data abundance and efficient data storage, ML rose to the limelight in recent times, but the foundational research in this field was done in seventy's and eighty's. Different ways for a computer to learn from data -- supervised learning, unsupervised learning, and reinforcement learning. A supervised learning algorithm takes labeled data while training the model, and then the model makes predictions in the presence of the new data. These problems could be divided into regression and classification problems.
One of the biggest lessons Australia and New Zealand business leaders can take from the past 12 months is that a climate of uncertainty is now the new normal. The shift in customer behaviour brought about by the COVID-19 pandemic, coupled with rapid information technology changes, has already presented significant challenges. As a result, many organisations have had to bring forward their digital transformation plans and complete projects in weeks or months rather than years. During 2021, CIOs will have to work throughout their organisations and apply digital technologies and data to unlock new business opportunities. They must also work to promote a growth mindset that will help to unlock fresh innovation and agility. Adopting such a growth mindset will require CIOs and IT teams to embrace six key trends during the coming 12 months.
The US Army just took a giant step toward developing killer robots that can see and identify faces in the dark. DEVCOM, the US Army's corporate research department, last week published a pre-print paper documenting the development of an image database for training AI to perform facial recognition using thermal images. Why this matters: Robots can use night vision optics to effectively see in the dark, but to date there's been no method by which they can be trained to identify surveillance targets using only thermal imagery. This database, made up of hundreds of thousands of images consisting of regular light pictures of people and their corresponding thermal images, aims to change that. How it works: Much like any other facial recognition system, an AI would be trained to categorize images using a specific number of parameters.