If your company isn’t using machine learning to detect anomalies, recommend products or predict churn, you will start doing it soon. Because of the rapid generation of new data, availability of massive amounts of compute power and ease of use of new ML platforms, we expect to see more and more applications that generate real-time predictions and continuously get better over time. Of the 100 early-stage startups we have met in the last six months, 90+ percent of them are already planning to use ML to deliver a better experience for their customers. ... Several high-profile experiments with ML and AI came into the spotlight in the last year. Examples include Microsoft Tay, Google DeepMind AlphaGo, Facebook M and the increasing number of chatbots of all kinds. The rise of natural user interfaces (voice, chat and vision) provide very interesting options and opportunities for us as human beings to interact with virtual assistants.
Little surprise, then, that some of the world’s biggest tech companies are testing their drones elsewhere. Last month, Amazon founder Jeff Bezos told a conference in Seattle: “We’re getting really good cooperation from the British equivalent of the FAA, the CAA [Civil Aviation Authority]. It’s incredible. It’s really cool.” Amazon is developing and testing its Prime Air delivery drones in the fields around Cambridge. Despite the UK’s enthusiasm for Amazon, and similarly permissive test flights elsewhere in Europe, Canada and Australia, “no single country stands out as being aeons ahead of everyone else,” says Holland Michel. “Though they may vary on the details, all countries are grappling with the same concerns. The deciding factor will be how flexible and responsive their regulations are.”
“The amazing thing about babies is they can see something once or hear a new word for the first time and they already have a good idea of what that new word could mean and how they could use that new word,” says Gopnik. “So these kind of Bayesian approaches have been good in explaining why children are so good at learning even when they don’t even have much data.” Babies use the probabilistic model to create a variety of hypotheses by combining probabilities and possibilities to draw conclusions. ... Older learners develop biased perspectives as they learn more about the world and strengthen certain neural connections, which hamper their ability to form out-of-the-box hypotheses and abstract theories based on little information. This is where babies and children under the age of five thrive.
There are so many possible applications of blockchain. You can literally transact anything. It could be used for welfare distribution, or for secure voting, land title transfer, music, movies, you name it. One good example that we are looking into is in vaccine distribution. There is a certain percentage of vaccines that get lost on their way to distribution, for numerous reasons. Where blockchain can be helpful is simple supply chain management. We could use blockchain to create an immutable record which is translated along the Bitcoin blockchain. Even if your merchandise got lost, you would know where you lost it. And while we are in the process of fixing a huge problem that impacts millions of people – especially children – around the world, we are also solving a problem that any company throughout the world grapples with on a daily basis.
Fintech startups are interested in APIs from banks and vice versa. What’s more, consumers are embracing this unity on a greater and greater scale. As a result, it’s no longer a matter of whether fintech startups or banks will win a fight against each other. Instead, it’s a matter of which companies will use the right combination of APIs to create something that consumers really want. Some argue that sharing of APIs may cause an unbundling of the legacy banking industry supply chain, allowing aggregators to select products and services to be reassembled in new ways. While that potential exists, others, like Ron Shevlin, believe traditional financial services firms hold the cards because of their existing customer relationships. He believes that the opportunity for platformification™ is powerful.
Today's smartphones are powerful computers that allow us to perform tasks that only a generation ago would have been considered science fiction. The devices also often contain a tremendous amount of confidential information, including the contents of our text and email communications, as well as access to various accounts via pre-logged-in apps. It is imperative, therefore, to keep the devices safe from hackers, and to take immediate corrective action if one's phone is breached. But how can you tell if your smartphone has been compromised? Below are some symptoms to look out for. Please keep in mind, however, that none of the clues that I discuss in this article exists in a vacuum, or is, on its own, in any way absolute. There are reasons other than a breach that may cause devices to act abnormally.
Most of the time, employees using Shadow IT applications don’t think they are doing any harm and that the apps themselves couldn’t possibly interfere with officially sanctioned IT products or company policies. But all too often Shadow IT apps don’t measure up to corporate standards for data protection and encryption. They can also consume a large amount of bandwidth which in turn can slow the network. In addition, Shadow IT can cause issues when it comes to compliance with data protection laws and sharing data directives. If that wasn't bad enough, the presence of Shadow IT apps on a corporate network dramatically increases the risk of security breaches and data loss that can hurt the company from a financial and reputational perspective.
Data virtualization enables unified data governance by creating a Virtual Data Services Layer across internal and external data sources, while leaving the source data where it is. A strong data management function sponsored centrally by the chief data office builds guiderails of standards, access control, certified provisioning points, and strong data governance. However, access to data is decentralized in a self-service model for business users, reporting and regulatory interface teams. This approach minimizes replication saving millions of dollars, but more importantly reduces the time and complexity to reach the objective of enhanced GRC. Industry leaders in every field are adopting data virtualization in the context of GRC to tackle the challenges of internal risk management, regulatory reporting, and enhanced agility in the face of changing business needs.
Sadly, Australia has turned into a huge target for ransomware; as Mr. Dillon pointed out, in terms of reported incidents, “Australia is currently ranked third and last time it was fifth, so it’s in a worsening position.” Lack of education is a cause, but it’s not the only one: “From an enterprise perspective, there is a skills shortage in Australia,” he said, adding, “I know a professional services organization that has just spent 18 months trying to fill a security application position.” And that leads to successful attacks, explained Ms. Boo, referencing one small business owner she talked to. “What happened is that he got exploited by ransomware. He did whatever people always asked him to do, always made sure he had a backup on his data. So what happened was he thought what he had was safe so when he got attacked, he refused to pay the ransom.
How could the computer learn to identify if a recorded voice is from a male or female? Well, if we want the computer to help us, in this case then we need to speak its language: numbers. In the machine learning world this means extracting features from the data. If you followed the Kaggle link above you can see that they already have extracted lots of features from the speech signal. Some feature examples are: mean frequency, median frequency, standard deviation of frequency, interquartile range, mean of fundamental frequency, etc. In other words, instead of having a time series showing the voice pressure signal, they extracted characteristics of this signal that may help us identify if the voice belongs to a male or female- this is called feature engineering. Feature engineering is a critical part of most machine learning processes.
Quote for the day:
"Better to be slapped with the truth than kissed with a lie." -- Russian Proverb