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The Natural Roots of Artificial Intelligence
To begin our exploration of AI we start with defining intelligence. The Oxford Universal Dictionary (1955) leans heavily on the word's Latin root, intelligere (to understand), defining intelligence as [1] the faculty of understanding; intellect; and [2] understanding as a quality admitting of degree; spec. While this focus on "understanding" does highlight the capacity to perceive meaning, it is overbroad; we need to look further for more clarity. Within academic circles, researchers do not have a universally shared definition of intelligence. Broadly speaking, there are four major viewpoints on intelligence that carry over to artificial intelligence research -- each with proponents and critics.
Black box problem stunting ML adoption in default risk analysis
Difficulties in explaining machine learning (ML) models is causing concern as banks look to the technology for default risk analysis, according to market participants. "Many different types of'black-box' models have been developed out there even by banks claiming that they can accurately predict mortgage defaults. This is only partially true," said Panos Skliamis, chief executive officer at SPIN Analytics in an email. "[These models] usually target a relatively short-term horizon and their validation windows of testing remain actually in an environment too similar to that of the development samples. However, mortgage loans are almost always long-term and their lives extend to multiple economic cycles, while the entire world changes over time and several features of ML models severely influenced by these changes of the environment," he said. The black box problem refers to the inability of an end user to understand the processes occurring between input and output in a machine learning model.
Traces AI is building a less invasive alternative to facial recognition tracking – TechCrunch
With all of the progress we've seen in deep learning tech in the past few years, it seems pretty inevitable that security cameras become smarter and more capable in regards to tracking, but there are more options than we think in how we choose to pull this off. Traces AI is a new computer vision startup, in Y Combinator's latest batch of bets, that's focused on helping cameras track people without relying on facial recognition data, something the founders believe is too invasive of the public's privacy. We can use your hair style, whether you have a backpack, your type of shoes and the combination of your clothing," co-founder Veronika Yurchuk tells TechCrunch. Tech like this obviously doesn't scale too well for a multi-day city-wide manhunt, and leaves room for some Jason Bourne-esque criminals to turn their jackets inside out and toss on a baseball cap to evade detection. As a potential customer, why forego a sophisticated technology just to stave off dystopia? Well, Traces AI isn't so convinced that facial recognition tech is always the best solution; they believe that facial tracking isn't something every customer wants or needs and there should be more variety in terms of solutions. "The biggest concern [detractors] have is, 'Okay, you want to ban the technology that is actually protecting people today, and will be protecting this country tomorrow?'
Trump economic adviser dismisses fears of looming recession
BERKELEY HEIGHTS, NEW JERSEY – President Donald Trump's top economic adviser is playing down fears of a looming recession after last week's sharp drop in the financial markets and predicting the economy will perform well in the second half of 2019. Larry Kudlow said in Sunday television interviews that consumers are seeing higher wages and are able to spend and save more. "No, I don't see a recession," Kudlow said. Let's not be afraid of optimism." A strong economy is key to Trump's reelection prospects.
Your data's just sitting there. Machine learning can change that.
Most financial institutions know it's critical to manage the ever-increasing amounts of accessible data, but many miss the potential in using that data in innovative ways. Financial institutions have a plethora of data they can access, either through their own systems or through public sources. However, many can't -- or won't -- exploit the large volumes of data, particularly the "owned" data that an organization holds about customers. This kind of data is typically called customer relationship management data, such as the purchase history tied to app installs, email addresses and postal addresses. Though financial institutions maintain and collect massive volumes of data, many firms are restricted from fully using that data because they are required to comply with stringent regulations around what can and cannot be done with customer data.
How Cities Should Prepare for Artificial Intelligence
It's time for city administrations and local employers to close AI-related skills gaps. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. While there is much discussion of how artificial intelligence will continue to transform industries and organizations, a key driver of AI's role in the global economy will be cities. How cities deal with coming changes will determine which ones will thrive in the future. Many cities have plans to become "smart cities" armed with AI-driven processes and services, like AI-based traffic control systems, to improve residents' lives.
Artificial intelligence to predict protein structure
Proteins are biological high-performance machines. They can be found in every cell and play an important role in human blood coagulation or as main constituents of hairs or muscles. The function of these molecular tools is obvious from their structure. Researchers of Karlsruhe Institute of Technology (KIT) have now developed a new method to predict this protein structure with the help of artificial intelligence. This is very difficult to detect, the experiments needed for this purpose are expensive and complex.
How AI is helping track endangered species Microsoft On The Issues
The Hawaiian poʻo-uli, a small bird from the honeycreeper family, was first discovered in 1973. Less than half a century later, it disappeared from the planet. Declared extinct in 2018, it is one of almost 700 vertebrate species that have been driven to extinction in the last 500 years. According to a United Nations report issued earlier this year to policymakers, one million species are at risk of extinction: Human actions threaten more plants and animals than ever before. Although the precise number of species on the planet is difficult to calculate, recent estimates put it at around 8.7 million.
Smart Farming, or the Future of Agriculture
We are a Ukraine-based company which means that our parents and grandparents lived in the era of infamous Soviet collective farms, where tractors were considered to be an ultimate technology. For them, a smart farm will sound like a fairy tale. So let it be, a fairy tale of a smart farm. First of all, what is a smart farm? Smart Farming is a concept of farming management using modern Information and Communication Technologies to increase the quantity and quality of products.