AI News, Is machine learning dying?
Is machine learning dying?
In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed".
Please notice that only the points(Support vectors) which lie on the margins make any contribution to future decision making, and no heed is given to rest of the points.
The ambit of Machine Learning is a bit hazy : Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.
The faith is on data -- the data has to be so rich that whatever new query will come will be similar to something already present in the data.
My understanding of the current scenario is a strong shift from ML techniques to Big Data/Data Driven techniques -- latter is thriving and the former is, lets face it, dying.
Here is what's up with ML recently : == Quoting : [1206.4656] Machine Learning that Matters -- Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society.
From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains.
== Google Flu Trends : Google predicts H1N1 flu using search terms Instead of using statistical data of H1N1 cases, Google has been using search queries as a starting point.
For example, if Google notices a spike in queries in its search engine with the words “fever,” “sore throat” and “aches” in Wichita, they might predict an increase in the number of swine flu cases there over the next couple of weeks.
[http://www.bmva.org/bmvc/2010/co...] This paper shows by experiments the superiority of an extremely naive method over the well established popular approaches with ever increasing complexity.
It stimulates us to rethink: Has the current advance of computer vision research(mostly ML and predictive models) touched the underlying problem in face recognition?
I have read half of a big book on Big Data, and worked 3 projects which dealt with fairly large amount of data.
Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.
The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.
Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.
Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.
Don’t make this big machine learning mistake: research vs application
It’s definitely a great direction to pursue for many businesses since it gives them the ability to deliver tremendous value in a fairly quick and easy way.
If you’re more of a technical person, do you go full throttle on learning how to do machine learning research?
To answer this question we need to differentiate between the two types of ways we can really work with machine learning: research and application.
If you were to tell them “We’re really good at automatically detecting human intruders using face recognition with 95% accuracy.
The Machine Learning engineer is often hidden in plain sight, having extensive experience in deploying cutting edge products and plus enough knowledge of machine learning to use it.
The Machine Learning engineer isn’t as fancy as the researcher since they don’t look like ML superstars with a PhD and 5000 citations.
Is the thing you’re building super custom, going beyond the current state-of-the-art in AI or in a totally different direction?
You don’t need someone to reinvent the wheel, you need someone who knows how to use the wheel to make your car better: an engineer!
Machine Learning does some really cool things now!… But it’s purpose is still primarily to eventually deliver some kind of value to consumers.
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ML and Big Data — Real-World Applications Machine learning — the branch of artificial intelligence that gave us self-driving cars — is helping businesses analyze bigger, more complex data to uncover hidden patterns, reveal market trends, and identify customer preferences for faster, more accurate results.
Predictive analysis enables doctors and clinicians to focus on providing better service and patient care, creating a proactive framework for addressing patient needs before they are sick.
Wearable technologies and sensors use data to assess patient health in real time, detecting trends or red flags that could potentially foresee a dangerous health event such as cardiac arrest.
Advancements in cognitive automation can support a diagnosis by quickly analyzing large volumes of medical and healthcare data, identifying patterns and connecting the dots to enhance treatment and care.
In March of 2017, the retail chain opened Store №8 in Silicon Valley, a dedicated space and incubator for developing technologies that will enable stores to remain competitive in the next five to ten years.
In the face of stiff competition, the automotive industry is taking steps to differentiate itself by leveraging ML capabilities and big data analytics to improve operations, marketing, and customer experience before, during, and after purchase.
By identifying trends and patterns from large datasets on vehicle ownership, dealer networks can be optimized by location for accurate, real-time parts inventory and improved customer care.
- On Monday, March 25, 2019
Artificial Intelligence Vs Machine Learning Vs Data science Vs Deep learning
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