AI News, The AI, machine learning, and data science conundrum: Who will ... artificial intelligence
The Privacy Conundrum: What Will You Give Up To Protect Your Identity?
Our very eyes and ears and hands seem to see, to hear, and to act, as if they belonged not to individuals but to the community.” Sensor networks, Artificial Intelligence (AI), Machine Learning (ML) and data analytics could make Plato’s desire and Popper’s disgust a reality.
New technologies beget new concerns, though human beings tend to accommodate risks for benefits like pleasure, productivity or convenience.
The regulation's interpretation and applicationwill change markedly over the next few years as it confronts other regulatory regimes, technology change, consumer behaviors and competition.
As an unintended consequence, clicking “European Citizen” on a site might effectively answer the question, “Would you prefer service or privacy?” Consider GDPR’s “right to be forgotten.” If requested, a company must remove an individual’s data from their systems, except insofar as it is required to accomplish services contracted or comply with other regulations.
Further, how will regulators expect companies to act when individuals request to opt out after ML systems have used their data as part of larger data sets to generate insights?
It’s not clear if any fines will eventually be assessed, but the fact that nearly anyone will be able to initiate suits under GDPR presents businesses with potentially unlimited jeopardy.
As the CIO of a major European corporation (who requested anonymity) recently commented to me, “That’s why Googles and Alibabas don’t grow in Europe.” Our Privacy Conundrum GDPR and its complications are part of thewider story of privacy in a connected age.
As I’ve argued elsewhere, with respect to data control, China’s regime makes government paramount, the U.S. generally favors corporations and Europe tends toward the individual.
As Vivek Wadhwa and Alex Salkever argue in Your Happiness Was Hacked, “the unhappy reality is that the options available are rapidly decreasing in utility and reward and increasingly herding us into habits of mindless consumption.” The desire of humans to have what they want, where and when they want it—and for free—generates competitive advantages for companies able to deliver.
A notion laughable in retrospect, though speed eventually contributed to over a million auto accident deaths each year in the U.S. What might become of our 21stcentury data-driven analog of traffic fatalities?
Artificial Intelligence: Could it help solve the Irish soft border conundrum?
The question of what happens to the border between Northern Ireland and the Republic of Ireland as the UK leaves the European Union is one of the most taxing challenges affecting the Brexit negotiations.
Artificial Intelligence (AI) is not something that magically produces an answer to every difficult question – however, it’s already in use elsewhere around the globe and offers innovative new techniques to address challenges facing border and port management should the United Kingdom exit the EU Customs Union.
The critical question is: without a physical border, can the UK maintain its constitutional and border integrity whilst honouring the Good Friday Agreement as a country outside of the EU customs union?
Bona fide truckers would clearly find the reduction in potential delays far outweighs any possible privacy concern and furthermore agreed data retention processes would be applied.
These systems would alert officials to take near real-time action to mitigate risks for vehicles so-equipped and allow other techniques to focus on the less high tech.
For example, telemetry, transactional and other kinds of structured data can be combined with video, voice and other forms of unstructured data to uncover more valuable answers to real-time questions.
More advanced use of AI would augment the commanders own experience and give guidance as to the most successful interventions available based on present resource deployment and historical records of similar cases.
Data Science, Machine Learning and Artificial Intelligence for Art
Data Science, Machine Learning and Artificial Intelligence are fields from computer science that have already penetrated many industries and companies around the world.
This blog post will explain how state-of-the-art data science, machine learning (ML) and artificial intelligence (AI) methods are being used in the art market by Thread Genius, a firm acquired by Sotheby’s, the oldest international auction house in the world (Est.
Artificial Intelligence is when computational tools start to possess cognitive abilities — for the purposes of this post, AI will refer to “deep learning” techniques that use artificial neural networks.
Our initial efforts involve software development of large scale data pipelines for cleaning and standardizing the troves of historical Sotheby’s data so that we can undertake data analysis and apply ML and AI at scale.
Sotheby’s has some of the best data in the art market related to historical transactions, individual’s preferences for art at every price point, images, object and artwork information, and much more.
The dataset uses the purchase prices of the same painting at two distinct moments in time (i.e., repeat-sales) to measure the change in the value of unique works of art.
By bringing all three of these missions together, our aim is to improve operational efficiency and build the best data products in the art market so that our clients can get the best experience and transparent information when engaging with art at Sotheby’s.
We primarily use Google Cloud Platform for all of our work — everything from data cleaning in Dataprep, from data processing and standardization in Dataflow, to data storage in Big Query, data analysis in Datalab, and finally, ML and AI using GCP’s whole suit of machine learning capabilities.
We are excited to be applying advanced machine learning and artificial intelligence in the art market and working directly with our Specialists at Sotheby’s so that we can create the best data products in the industry.
If you have a background in data science, machine learning, NLP and/or AI and are interested in changing the world, feel free to reach out to us for a chat, we’re always interested in speaking with you, our audience.
Artificial Intelligence vs Machine Learning vs Data Science
Modern technologies like artificial intelligence, machine learning, data science andbig data have become the buzzwords which everybody talks about but no one fully understands.
For instance, general AI would mean an algorithm that is capable of playing all kinds of board game while narrow AI will limit the range of machine capabilities to a specific game like chess or scrabble.
General AI is just a dream of researchers and perception among the masses that will take a lot of time for the human race to achieve (if ever possible).
Machine learning focuses on enabling algorithms to learn from the data provided, gather insights and make predictions on previously unanalyzed data using the information gathered.
In case of supervised learning, labeled data is used to help machines recognize characteristics and use them for future data.
For instance, if you want to classify pictures of cats and dogs then you can feed the data of a few labeled pictures and then the machine will classify all the remaining pictures for you.
It uses various techniques from many fields like mathematics, machine learning, computer programming, statistical modeling, data engineering and visualization, pattern recognition and learning, uncertainty modeling, data warehousing, and cloud computing.
Data Science does not necessarily involve big data, but the fact that data is scaling up makes big data an important aspect of data science.
The practitioners of data science are usually skilled in mathematics, statistics, and programming (although expertise in all three is not required).
At NewGenApps, we focus on developing new age solutions that leverage these technologies and help you solve real-world business problems.
- On 2. marts 2021
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