AI News, Artificial Intelligence

10 trends of Artificial Intelligence (AI) in 2019

The Gmail email response prompter, Alexa in your kitchen, the transcription of your voicemails, automatic voice calls to your hairdresser for your next appointment and of course self-driving cars on the roads of Utah!

Lyft uses a series of deep models for the fraud detectionthat can be in the form of using stolen credit cards to generating transient rise requests.

In a recent survey by Deloitte(Q3 2018), it was very clear that “Early adopters are ramping up their AI investments, launching more initiatives, and getting positive returns.” Here is a look at how AI is helping organizations.

Supervised learning involves learning with labeled datasets to produce an output that is generic to that dataset (for example finding the price of a new house given the housing prices of a specific location).

Unsupervised learning involves finding the connections between unlabeled data or clustering that data (think of a bunch of images that are not labeled but have parameters like color, size etc.

We have a long way to go in terms of dealing with the challenges of quantum computing like maintaining coherence of the qubits or removing the unnecessary and noisy computations.

According to Andrew Childs, Co-director, Joint Center for Quantum Information and Computer Science (QuICS), “Current error rates significantly limit the lengths of computations that can be performed, We’ll have to do a lot better if we want to do something interesting.” The interesting problems could be to solve almost unsolvable problems like climate change, presence of earth-like planets in the galaxy or our body’s ability to destroy cancer.

Some of them include path planning, eye-tracking to improve driver monitoring, natural language processing to understand voice commands and maybe even self-direct itself to a gas station when running low on fuel.

For example, Enigma is a startup that allows organizations to a secure data marketplace that users can subscribe to and consume via smart contracts.

Facial recognition is a form of artificial intelligence application that helps in identifying a person using their digital image or patterns of their facial features.

These next-generation image recognition technologies can be used for healthcare purposes as well — to follow through clinical trials as well as medical diagnostic procedures.

Biased data: This topic is becoming increasingly important as machine learning models are being used for decision-making such as hiring, mortgage loans, prisoners released from parole or the type of social service benefits.

Some of the ways this issue can be prevented is by ensuring 1.active inclusion from diversity of inputs 2.review of potential risks and 3.balancing transparency in lieu of speed or performance.

For example, reading someone’s handwriting comes easily and unconsciously to us whereas in order to teach an algorithm we feed it with vast amounts of handwritten data to recognize patterns in it.

There is a huge demand of neural networks in robotics, to improve order fulfillment, prediction of the stock market, and diagnosis of medical problems or even to compose music!

Neural networks are also fundamental to “deep learning”, powerful set of algorithms that can be used for image processing, speech recognition or to process natural languages.

Socio-economic models: At every AI event I organize or attend, a common question on everyone’s lips is “would AI take away our jobs?” The answer in a nutshell is “it depends”.

While AI would take away mundane jobs where there is a scarcity of resources (e.g., agriculture, manufacturing or warehouse assistants), it would also enable newer jobs with different skillsets.

For example, although automation might remove the need for certain jobs, there would also be a demand for high-touch jobs such as customer service representatives, teachers, caregivers etc.

With the advent of both Amazon’s Alexa and Google home, there is a wide range of voice-enabled applications that use natural language processing (NLP) algorithms, an example of deep learning.

This has increased the interest in the next generation of deep learning algorithms that can solve even tougher problems such as interpreting technology infrastructure issues.

The growth of it continues as we enter 2019 and the focus would not only be on new technologies and applications in the industry but also how it intersects with the society and heralds in technology for the better.

Artificial Intelligence Can Detect Alzheimer’s Disease in Brain Scans Six Years Before a Diagnosis

Using a common type of brain scan, researchers programmed a machine-learning algorithm to diagnose early-stage Alzheimer’s disease about six years before a clinical diagnosis is made – potentially giving doctors a chance to intervene with treatment.

In a recent study, published in Radiology, Sohn combined neuroimaging with machine learning to try to predict whether or not a patient would develop Alzheimer’s disease when they first presented with a memory impairment – the best time to intervene.

Other types of PET scans look for proteins specifically related to Alzheimer’s disease, but glucose PET scans are much more common and cheaper, especially in smaller health care facilities and developing countries, because they’re also used for cancer staging.

“Given the strength of deep learning in this type of application, especially compared to humans, it seemed like a natural application.” To train the algorithm, Sohn fed it images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a massive public dataset of PET scans from patients who were eventually diagnosed with either Alzheimer’s disease, mild cognitive impairment or no disorder.

“However, before we can do that, we need to validate and calibrate the algorithm in a larger and more diverse patient cohort, ideally from different continents and various different types of settings.” If the algorithm can withstand these tests, Sohn thinks it could be employed when a neurologist sees a patient at a memory clinic as a predictive and diagnostic tool for Alzheimer’s disease, helping to get the patient the treatments they need sooner.

Artificial Intelligence in Radiology: What you need to know Part 1

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Artificial Intelligence in Radiology: What you need to know Part 2

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