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.

Many large enterprises realize that their data can give them rapid, useful insights for implementing changes that will benefit their business. But as the volume of data grows, it becomes more difficult for these companies to extract meaning from it. The solution is industrial machine learning (IML), which can consistently produce data-driven insights at enterprise scale. IML can ingest data, build algorithms, deploy them into production and generate continuous insights into ongoing business problems. IML is a modern take on a very old idea: the scientific method.

More frequently we read news alerts about cyber attacks. The scary notion is that the cyber world has become a dangerous arena where most anything goes. Businesses and governments are trying to secure their online systems as quickly and effectively as possible against these digital risks. Unfortunately, the infiltrations these parties fear is not just enemies and hackers, but competitors and others looking to read confidential emails, steal secrets, or simply commit fraud. I was recently asked to share my thoughts on what could help mitigate risk in a technology driven world.

Marketers are usually the ones responsible for creating the buzzwords that follow us everywhere we go; that is, until AI turned the tables and made the whole marketing industry buzz with excitement. Your email address will not be published.

Apparently Google Translate, the company's popular machine-translation service, had suddenly and almost immeasurably improved. Rekimoto visited Translate himself and began to experiment with it. He had to go to sleep, but Translate refused to relax its grip on his imagination. Rekimoto wrote up his initial findings in a blog post. First, he compared a few sentences from two published versions of "The Great Gatsby," Takashi Nozaki's 1957 translation and Haruki Murakami's more recent iteration, with what this new Google Translate was able to produce.