Artificial intelligence is gaining traction faster than anybody imagined – quickly knocking down the dominoes of complex tasks that computers have long struggled with. For example, in April this year, Wired Magazine ran an article explaining why AI still sucked at transcribing speech. But just six months later, AI is now as good as humans at listening! And there have been similarly dramatic advances in other areas. Adding machine learning to Google Translate has improved error rates by up to 85% since it was introduced a month and a half ago! "It has improved more in one single leap than in 10 years combined" -- The list goes on: Google's AI can already lip read better than people, and the company's image service is now better than people at recognizing and tagging images.

Our SMS search engine, called Text Engine, was originally created in 2013. The idea was to create a utility that would enable users to search the Web without needing to use a web browser and without using data. Text Engine accomplishes this by giving you access to vital, basic web information just by sending and receiving text messages. To keep Text Engine relevant for an ever-changing mobile market, our investors suggested that we think about adding a chatbot experience to Text Engine. So that's what we did.

When industry leaders launched a non-profit organization in September to create best practices for artificial intelligence, one company was notably missing. On Friday, the Partnership on AI announced that Apple had joined as a founding member. The organization, which will publish research on the ethics, transparency and reliability of AI, among other things, was initially made up of tech giants Amazon, Microsoft, Facebook, Google and IBM. Though Apple collaborated with the partnership since before it was announced last year, it wasn't officially a member until now. Tom Gruber, Apple's head of advance development of Siri, will represent the company. "We're glad to see the industry engaging on some of the larger opportunities and concerns created with the advance of machine learning and AI," Gruber said.

The secrets of a hidden room in Coughton Court, a Tudor mansion linked to the plot to assassinate King James I in 1605, have been revealed in a new study. The double room was a hiding place for priests during the anti-Catholic persecutions of the 16th and 17th centuries and was leased by Sir Everard Digby, one of the leading conspirators of the plot. The room was discovered in the 1850s, but has now been revealed in more detail than ever before with the help of 3D laser scanners. The secrets of a hidden room in Coughton Court, a Tudor mansion linked to the plot to assassinate King James I in 1605 have been revealed in a new study. In 1605 Coughton Court was leased to Sir Everard Digby, one of the leading conspirators of the plot to blow up the House of Lords and kill King James I. On the evening of the plot, Sir Everard's wife was waiting anxiously for news at Coughton alongside Father Henry Garnet, the head of the Jesuit mission in England, and Nicholas Owen, the celebrated priest-hole maker.

Apple has joined an artificial Intelligence (AI) research group alongside the other tech giants of the world. The organization, called Partnership on AI, ensures that the technology is used ethically and for the greater good of humanity. The Cupertino company has been involved with the group since last year, but has formalized its membership together with Amazon, Facebook Google/DeepMind, IBM and Microsoft. Apple has joined an artificial Intelligence (AI) research group alongside other tech giants of the world. The birth of AI has made our lives easier, but the more advance it becomes, the more some worry about what the future holds.

An artificial intelligence watchdog should be set up to make sure people are not discriminated against by the automated computer systems making important decisions about their lives, say experts. The rise of artificial intelligence (AI) has led to an explosion in the number of algorithms that are used by employers, banks, police forces and others, but the systems can, and do, make bad decisions that seriously impact people's lives. But because technology companies are so secretive about how their algorithms work – to prevent other firms from copying them – they rarely disclose any detailed information about how AIs have made particular decisions. In a new report, Sandra Wachter, Brent Mittelstadt, and Luciano Floridi, a research team at the Alan Turing Institute in London and the University of Oxford, call for a trusted third party body that can investigate AI decisions for people who believe they have been discriminated against. "What we'd like to see is a trusted third party, perhaps a regulatory or supervisory body, that would have the power to scrutinise and audit algorithms, so they could go in and see whether the system is actually transparent and fair," said Wachter.

Apple has joined the Partnership on AI to Benefit People and Society, and will work alongside other tech giants on artificial intelligence initiatives, the partnership announced Friday. Apple previously worked with Partnership on AI, but Friday's announcement makes the company's membership official, as it joined as a founding member. Apple has not yet announced the partnership. Partnership on AI, a nonprofit, was launched by Amazon, Facebook, Google, IBM, and Microsoft in September 2016. The organization works to address "opportunities and challenges with AI technologies to benefit people and society."

Apple has joined rivals as it takes a step ahead to advance research and development of artificial intelligence technologies. After months of collaborating, Apple is joining the Partnership on AI, with other founding members including Google, IBM, Microsoft, Facebook, and Amazon. The Partnership of AI was founded in September last year to also steer debate on best practices on AI. The group believes AI could help in the areas of health care, transportation, and automation in factories. Apple's most visible AI technology is Siri, a voice assistant that can answer questions.

Let's start with a simple problem, we suppose that we have a small dataset with house prices for a specific area in a city, the database contains two fields, the size of the house and its price (SIZE, PRICE), and I would like to know the price of a house with a specific size, the problem is that I don't have that size in my dataset, what should I do? We already know from the title that the solution is linear regression, but to explain more easier, I've a collected a little dataset that contains house prices, in the table below a snippet from the dataset: Visualization helps us a lot in identifying patterns in data, that's way to have a better view to our dataset, I m going to plot it using matplotlib python library: From the plotting we can see that the price grows with the size, but the points don't make a prefect line that can help us predict the price of a new size, so we need to find a linear function h(x) that passes next to all the points but not necessary over them, we call the function the hypothesis: In the equation 2, m is the size of our dataset, Xi is the ith price and Yi is the ith size in the dataset, we call J the error function (or the objective function) that we need to minimize. There are other error functions or estimators in statistics that we can use, but in our case we'll use the MSE or the mean squared error estimator, because it will help us find our unknowns parameters more easier, our function will become: The estimator J takes two arguments, which means it's a 3D function, the figure 3 shows how the function looks like in a 3D graph, our goal here is to find the minimum value, which is the lowest point in the graph below, imagine putting a ball inside the graph, the ball will slide into the bottom of the shape. To find the lowest point in the shape, or in another word minimizing the objective function, we'll use the gradient descent algorithm, which is very simple to understand. To reach the bottom of the shape, we will choose randomly a point in the graph, that's mean setting θ0 and θ1 to a random value, at that point we need to decide, do we need to go up or down?

This piece was coauthored with Megan Beck, Chief Insights Officer at OpenMatters, and Steven Cracknell, advisor to OpenMatters. For most of the history of business, leaders and organizations have focused on the physical--extracting, manufacturing, and selling goods. Today, technology has advanced to enable companies to trade on the intangible, but ideas and relationships are much harder to measure and manage than production and inventory, which has left many leadership teams without a compass. Luckily, machine learning and big data analytics are helping executives close the gap and manage the intangible. Data science used to be expensive and academic, yet today technology is enabling organizations to (1) gather and (2) analyze more data than ever.