Opinion


Guest Editorial: Discovery and Artificial Intelligence : American Journal of Roentgenology : Vol. 209, No. 6 (AJR)

#artificialintelligence

I thank Keith Dryer and Bradley Erickson for their expertise, leadership, and educational efforts in the applications of AI in radiology, all of which have helped me understand this complex subject more fully.


Guest editorial: The next space race is artificial intelligence

#artificialintelligence

Second, our regulatory regime makes it more difficult to build things in the United States and sell them to other countries, creating a market for foreign competitors who would otherwise not stand a chance. For years, the United States curbed exports of encryption technology and basic processors. This only led international competitors to fulfill demand, creating a market for themselves. When U.S. allies like Saudi Arabia, Pakistan, the United Arab Emirates, and Turkey needed access to unmanned aerial systems to prosecute the war on terror, these requests were delayed or denied. We have since lost almost all of these markets to Chinese exports and indigenous development.


Rethinking 3 Laws of Machine Learning

#artificialintelligence

With the ever-increasing volume of electronic data in today's organizations, end users lack the time to practice intelligent information governance. It has become a universal truth that the gargantuan amount of corporate data has far exceeded human ability to effectively manually manage it. Fortunately, in the not-too-distant future lies the possibility of Predictive Information Governance (PIG). Ideally, it results in intelligent, near error-free automation that keeps end users from having to make decisions that, due to information overload, they are no longer qualified to make, such as whether or not to retain documents, how long to keep them, and where to safely store them. I believe that the reliance on end users is the weakest link in most intelligent automation and supervised machine learning technology platforms.


The outcome of this virtual riot depends on your emotions

Engadget

In RIOT 2, an interactive film by Karen Palmer, controlling these emotions is the key to your escape. Yet the ongoing melding of games and film into interactive narratives raises the question of how we should control these new experiences naturally. "Conversation, facial expression, intonation of our voice, physical gesture -- all of those are the natural language of human interaction. "In my opinion, fear is the most powerful emotion," Palmer, originally from London, said.


How Apple is taking AI to the masses

#artificialintelligence

If you talk to our various teams -- the software team, Apple TV team, the mail team, the home pod team -- all of them have AI projects going on. Apple's AI roots date back to the mid-1990s with handwriting recognition on the Newton," says Gene Munster, Founder of research firm Loup Ventures. In June, Apple announced Core ML, a platform that allows application developers to easily integrate machine learning (ML) into an app. Apple reckons that its ownership of hardware, software and the silicon gives it a huge advantage over rival players who may have ownership of one or two elements of the ecosystem.


Meet the Netflix of Big Data & Data Science

@machinelearnbot

KDnuggets has the 7 Steps and Key Terms, Explained series (among others), as well as numerous in-house one-off tutorials and opinion pieces covering a wide variety of machine learning, data science, Big Data and AI topics, such as 10 Free Must-Read Books for Machine Learning and Data Science and Machine Learning overtaking Big Data? Netflix finds quality content from elsewhere and runs it as guest programming in order to increase reach, just as KDnuggets does with republished guest posts. Netflix syndicates quality content originally found elsewhere, which has allowed so very many British shows to make their way to North America for mass consumption, as but one example of this. KDnuggets also runs quality tutorials, overviews, and opinion pieces from other blogs and sites around the web, in order to increase their exposure.


Hard numbers: The mathematical architectures of Artificial Intelligence

#artificialintelligence

Above statistics we have data mining, which is the process of using generalised algorithms to find patterns in data. Wherever you set the bar, I guarantee that you will find that the system you are calling AI is heavily dependent on machine learning, which only works if we have data mining, which relies heavily on statistics, which is fundamentally founded on maths. We have a good understanding of machine learning so any system that simply learns a set of rules from raw data and then applies them is not AI; it is machine learning. From the evidence I have seen I don't believe that Fukoku Mutual Life Insurance has an AI system; it sounds like machine learning to me.


Hard numbers: The mathematical architectures of Artificial Intelligence

#artificialintelligence

Above statistics we have data mining, which is the process of using generalised algorithms to find patterns in data. Wherever you set the bar, I guarantee that you will find that the system you are calling AI is heavily dependent on machine learning, which only works if we have data mining, which relies heavily on statistics, which is fundamentally founded on maths. We have a good understanding of machine learning so any system that simply learns a set of rules from raw data and then applies them is not AI; it is machine learning. From the evidence I have seen I don't believe that Fukoku Mutual Life Insurance has an AI system; it sounds like machine learning to me.


10 Data Science, Machine Learning and IoT Predictions for 2017

#artificialintelligence

Data science and machine learning will become more mainstream, especially in the following industries: energy, finance (banking, insurance), agriculture (precision farming), transportation, urban planning, healthcare (customized treatments), even government. Some, with no familiarity with data science, will want to create a legal framework about how data can be analyzed, how the algorithms should behave, and to force public disclosure of algorithm secrets. I believe that they will fail, though Obamacare is an example where predictive algorithms were required to ignore metrics such as gender or age, to compute premiums, resulting in more expensive premiums for everyone. The rise of sensor data - that is, IoT - will create data inflation. Data quality, data relevancy, and security will continue to be of critical importance.


10 Data Science, Machine Learning and IoT Predictions for 2017

@machinelearnbot

Data science and machine learning will become more mainstream, especially in the following industries: energy, finance (banking, insurance), agriculture (precision farming), transportation, urban planning, healthcare (customized treatments), even government. Some, with no familiarity with data science, will want to create a legal framework about how data can be analyzed, how the algorithms should behave, and to force public disclosure of algorithm secrets. I believe that they will fail, though Obamacare is an example where predictive algorithms were required to ignore metrics such as gender or age, to compute premiums, resulting in more expensive premiums for everyone. The rise of sensor data - that is, IoT - will create data inflation. Data quality, data relevancy, and security will continue to be of critical importance.