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) …
The Great Barrier Reef is the world's largest coral reef ecosystem and one of the seven natural wonders of the world. However, it is under great threat. A recent study by the ARC Centre of Excellence Coral Reef Studies revealed that the Great Barrier Reef has lost half its coral in the past three decades to mass bleaching events caused by rising water temperatures. Reef corals have an annual reproduction event every November and December that has the potential to see healthy coral spread their larvae, with the help of the ocean's current, to parts of the reef that have been affected by bleaching. The challenges faced by researchers are figuring out how to identify and map these healthy reefs, how to evaluate the way reefs can be protected, and how to monitor the dangers faced by corals.
In machine learning terminology, the sum of squared error is called the "cost". This equation is therefore roughly "sum of squared errors" as it computes the sum of predicted value minus actual value squared. The 1/2mis to "average" the squared error over the number of data points so that the number of data points doesn't affect the function. See this explanation for why we divide by 2. In gradient descent, the goal is to minimize the cost function. We do this by trying different values of slope and intercept.
If you're worried about facial recognition firms or stalkers mining your online photos, a new tool called Anonymizer could help you escape their clutches. The app was created by Generated Media, a startup that provides AI-generated pictures to customers ranging from video game developers creating new characters to journalists protecting the identities of sources. The company says it built Anonymizer as "a useful way to showcase the utility of synthetic media." The system was trained on tens of thousands of photos taken in the Generated Media studio. The pictures are fed to generative adversarial networks (GANs), which create new images by pitting two neural networks against each other: a generator that creates new samples and a discriminator that examines whether they look real. The process creates a feedback loop that eventually produces lifelike profile photos.
Buoy Health, a Boston, MA-based AI-powered healthcare navigation platform, today announced the completion of a $37.5 million Series C funding round. Cigna Ventures and Humana led the funding round and were joined by Optum Ventures, WR Hambrecht Co, and Trustbridge Partners. To date, Buoy has raised $66.5 million. Today, hospitals and insurance companies are increasingly investing in digital health innovations like Buoy to solve problems related to accessing the healthcare system and helping patients to get to the right care setting on the first attempt. Founded in 2014 by a team of doctors and computer scientists working at the Harvard Innovation Laboratory, Buoy Health uses AI technology to provide personalized clinical support the moment an individual has a health concern.
See also our related columns The Turning Point, Techie Tuesdays, and Storybites. Though artificial intelligence (AI) may not surpass human intelligence for at least a few more decades, it opens up opportunities and challenges that we must address today in order to shape a better world for us all. A call to action for business leaders, entrepreneurs, academics, and policymakers is effectively made in Toby Walsh's new book, 2062: The World that AI Made. The rise of AI poses serious philosophical, economic and social questions for all of us, and more vision and collaboration are urgently called for. How many jobs will AI take away or create?
Artificial intelligence (AI) projects including research into the early detection of cancer have received £20 million in funding from the Government. The Turing AI Acceleration Fellowships have been awarded to 15 researchers using AI on innovative and diverse projects, including on energy efficient data processing and increasing workplace productivity. The fellows include Professor Christopher Yau, at the University of Manchester, who aims to use artificial intelligence technology to predict cancer development inside the body before it has fully formed. Professor Damien Coyle, from Ulster University, has also been given a fellowship for his work developing AI technology for use in wearable neurotechnology. By measuring signals from the brain without needing movement, the technology could help people who are unable to communicate after a serious injury or illness.
Machine learning can be deployed virtually anywhere there's data. You've likely worked with tools like Google Analytics, Typeform, Hubspot, and others. In fact, machine learning can be used to make all of these tools more powerful, whether it's predicting traffic and conversions, predicting any data in a Typeform, or even driving growth hacking. Nowadays, AutoML tools seem to be taking over the world. If you Google "AI without code," you'll find tools like RunwayML for creative applications and Obviously.AI for tabular data, besides the more well-known Google AutoML tools (though, to be fair, Google's version is fairly hard to use).
There has been considerable recent progress in protein structure prediction using deep neural networks to infer distance constraints from amino acid residue co-evolution1–3. We investigated whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occuring proteins used in training the models. We generated random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting distance maps, which as expected are quite featureless. We then carried out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (KL-divergence) between the distance distributions predicted by the network and the background distribution. Optimization from different random starting points resulted in a wide range of proteins with diverse sequences and all alpha, all beta sheet, and mixed alpha-beta structures.
Sian Williams, Audience Strategy and Planning Director at MediaCom North, explains how AI offers a future of increased automation, faster decision-making and'hyper-personalisation' that will make marketing comms more effective and businesses more efficient. Robots are science fact not fiction, machines may well inherit the earth, and artificial intelligence can actually enhance the way we engage with human intelligence. As more interactions become digitised, the data landscape is only getting bigger and AI, and within it machine learning, will increasingly fuel that growth. The two terms are often used inter-changeably but whilst AI creates the structure of computational human intelligence it is machine learning that, sans specific programming, delivers on how quickly and effectively data will be processed and decisioned against. If AI is the brain itself, full of raw potential, then machine learning is at least one of the'cortexes' able to process information and develop intelligence and skill by using one'experience' to inform another.