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Artificial Intelligence 2018 - A Year in Review

| Author , tagged in Machine learning
Alegion

2018 was big year for Artificial Intelligence. AI, Machine Learning, Natural Language Processing, robots, drones, data… it was all in the news, all of the time. 

2018 saw stories in a huge cross section of publications. Technology standards like ZDNet and TechTarget covered the business of AI. The interest in AI technology breakthroughs spanned many other publications, including Fortune, the Wall St Journal, and Fast Company. With a little bit of digging into these articles, you’ll see that much of what has been garnering headlines is exploration, a proof of concept, or a University research project. In other words, for many organizations it is still early days for AI. 

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Discovering the value of Artificial Intelligence

One of the big AI headline grabbers this year was healthcare, and within healthcare, radiology is the frontrunner. If you’ve ever stubbed a toe trying to navigate your living room in the dark, contracted pneumonia, or gone to the dentist, you've probably been x-rayed.

Multiply your experiences by several billion, and you've got a lot of x-rays. Imagine the amount of data contained in those images that can be used to train an algorithm to identify fractures, pneumonia, or a cavity. Medical researchers have already imagined that, and are exploring ways to use computer vision to read x-rays and and other medical images to reduce the cost of healthcare and improve the patient experience. Here are some examples of articles published this year on radiology.

“Like other subspecialties in medicine, Radiology faces its own unique set of ongoing challenges. From decreasing reimbursement for radiology reports and procedures to increasing physician burnout, there are many pressing issues in the radiology field.”
“Researchers have shown that machine learning can identify if a patient has schizophrenia by analyzing an MRI of their brain, according to a new study published in Molecular Psychiatry.”
“Recent advances in artificial intelligence have led to speculation that AI might one day replace human radiologists. Researchers have developed deep learning neural networks that can identify pathologies in radiological images such as bone fractures and potentially cancerous lesions, in some cases more reliably than an average radiologist. For the most part, though, the best systems are currently on par with human performance and are used only in research settings.”

One Step Forward, 2 Steps back

We’ve written and spoken a lot about bias in AI this year. Failing to detect bias in ML training data - or even worse, Inserting bias into the data - jeopardizes the performance of AI models and can lead to highly undesirable results. In July, an article was published about how AI could revolutionize HR by weeding out human bias. Organizations are under enormous public and legal pressure to address bias in their hiring process. Taking humans and their own personal and cultural biases out of the equation could go a long way in leveling the hiring playing field.

Ironically, a prominent pioneer in this area got burned in 2018. Amazon’s AI HR system had to be shut down because while the humans didn’t add any bias, the historical hiring data used to train their model was full of it.

So Many Questions, so Few Answers

While AI has been extensively covered this year, the most common item in the headlines was a question mark. “Can we?”, “Should we?”, “Are we?”, were recurring questions that underscored the extent to which the public's embrace of AI technology is not as advanced as the technology itself. Still, these are good questions, and we need to keep talking about their answers. 

“Recent advances in artificial intelligence (AI) and computer technology are causing us to think again about some really basic questions: what is a firm? What can firms do better than markets? And what are the distinctive qualities of firms in a world of smart contracts and AI?”
“Artificial intelligence (AI) has been around longer than most people realize. The intent behind much of AI is to free us from mundane repetitive tasks, giving us more time to grow our intellects and businesses, with more interesting, evolving actions.”
“The department believes AI can help minimize delays by speeding information retrieval and improving the quality and accuracy of information provided.”

Learn more about AI in 2018

We’re looking forward to what 2019 has in store.

 

 

Topics: Machine learning