It is no brainer that the e-commerce market has transformed the Indian market like never before. Thanks to factors like rising smartphone penetration, the launch of 4G network, and increasing consumer wealth, analytics-driven customer engagement, and digital payment, the e-commerce sector is on an upward trajectory. It is projected that this industry will surpass the US to become the second-largest E-commerce market in the world by 2034. According to the PWC survey, with Internet penetration expected to almost double to 60% by 2022, the nation is arguably the world's most promising Internet economy, with a rapidly increasing'netizen' population. Further owing to improving data affordability, consumption growth, and newer financial products, the e-commerce market is set to grow, be it across e-tail, travel, consumer services or online financial services.
Google says every song's melody is like the song's unique fingerprint. It gives the song a unique identity that can be detected by Google's algorithms. The company has spent some time building a machine learning model that can match hums, whistles, or even singing to the right song. "When you hum a melody into Search, our machine learning models transform the audio into a number-based sequence representing the song's melody," the official Google Blog post explains. Google says its models are trained to identify songs on a variety of sources – human singing, whistling, humming, and studio recordings.
The Machine Learning is now in a phase of continuous expansion, facilitated by the offers of all cloud platforms. In my first and second articles about this argument, we found out that a programmer can analyze data using high-level tools, even without a vast knowledge of statistics and machine learning. Presuming that everything going to work at the first attempt is quite unlikely, that is, we can build a model with our set of data with reasonable efficiency. In the last article about ML, I introduced the feature crossing idea, which leads us to come back to the manipulation of data. In this situation, high-level tools show their limits and make us search for new ones: they are such complicated that we are not able to handle options and wizards, which suddenly fail (I wrote about this problem in my second article).
The launch of the Data Analysis Laboratory by stc is the first initiative of its kind in the Kingdom. It will help create a cooperative environment for the analysis of big data and the development of artificial intelligence models with all the local, regional, or international partners, whether they were academic or research entities. It will do so through the Laboratory's advanced capabilities that will provide creative and innovative opportunities in the various fields where AI is implemented and help deal with big data to serve multiple fields. It will also contribute to strengthening the Kingdom's competitive position and improving the development of local capacities by enabling various initiatives to serve the Kingdom's data society, such as the development of young Saudi skills inside and outside the company.
According to the verge, perhaps the most important of them is: the new spell check tool that Google promises will help identify even the most bad spelling queries. Prabhakar Raghavan, head of Google's search department, revealed that 15% of Google's search queries every day are those that Google has never seen before, which means that the company has to work constantly to improve its results. Part of that is due to poor spelling queries, as Cathy Edwards, Google's vice president of engineering, points out, as 1 in 10 search queries on Google are misspelled, and Google has always tried to help with the "do you mean" feature that suggests correct spelling. A huge update will be rolled out by the end of the month, which uses a new spelling algorithm powered by a neural network with 680 million teachers, and runs in less than three milliseconds after each search, and the company promises that it will provide better suggestions for misspelled words. For example, if users search for the phrase "How can I determine if my home windows are UV glass?"
To train a GPT-2 neural network, first of all we need to pre-process the data, in order to obtain a single .txt For the sake of simplicity and since the machine learning model we will use requires a GPU to work, we're going to use Google Colab for the next step. If you don't know what Google Colab is, check this other article here. To work with the data, we need to upload them on Colab, into the right folders. Now, run all the cells up until the block "2 Parse the data".
Transaction data is like a friendship tie: both parties must respect the relationship and if one party exploits it the relationship sours. As data becomes increasingly valuable, firms must take care not to exploit their users or they will sour their ties. Ethical uses of data cover a spectrum: at one end, using patient data in healthcare to cure patients is little cause for concern. At the other end, selling data to third parties who exploit users is serious cause for concern.2 Between these two extremes lies a vast gray area where firms need better ways to frame data risks and rewards in order to make better legal and ethical choices.
Can artificial neural networks learn logic? Today's #Machinelearning running #neuralnetworks is mere "a method of #dataanalysis that automates analytical model building". It can NOT learn from data, or "recognize patterns" or "make decisions" with minimal human intervention. It is an #algorithms that improve automatically through "training data" to fit some parameters; or the computing ability to automatically apply complex mathematical calculations to #bigdata. In such a ML, a common task is the study & construction of algorithms that can INTERPOLATE from and make ESTIMATIONS on #data.
How are Machine Learning Models going to change the Payments Industry? It wasn't so long ago that CEO's and large commercial banks were convinced that more bank locations would always be necessary to service and acquire new customers. However, in the last ten or five years we have seen an emergence of Digital Banks, that have never and will probably never own a physical location, but still manage to grow their user base and add additional services including insurance, mortgages, and loans. In the Banking industry, we have seen companies like First Bank of Nigeria, United Bank of Africa, Zenith Bank, Guaranty Trust Bank dominate for well over twenty years. However, just like the digitization of banking has forced incumbents to change their strategies, the digitization of payments has provided companies like Flutterwave, Paystack, Remita and lately even Korapay to take up some of the market shares, not by focusing on traditional businesses, but by focusing on startups who have grown to overshadow and sometimes even bankrupt traditional businesses.
Tinder, the most popular dating app in the world, has banned teens under the age of 18 but it's not stopping them from signing up. A Massachusetts man is accused of kidnapping and assaulting a woman he met on Tinder, threatening to kill her and her child if she went to the cops, authorities said. Peter Bozier, 28, was arrested Tuesday during a traffic stop in Sudbury after the victim told investigators she was severely beaten and strangled while being held against her will at Bozier's home, police said. The victim said the harrowing ordeal began a day earlier, police spokesman Lt. Robert Grady told the MetroWest Daily News. Grady said the woman managed to "release herself from the situation" and then went to a hospital in Burlington, where hospital staffers contacted police, the newspaper reported.