We have all been warned about identity theft. Even the big banks like Barclays are running TV ad campaigns showing customers how a phone call that seems to legitimately come from your bank can be used to steal your online banking pin number. There is plenty of information out there about how to keep your details safe, but no matter what precautions we take, there are always bad actors out there (this is now a ‘polite’ way of referring to people who are nothing more than criminals) who are relentless in their search for new ways to access our private information.
Tomislav Markovski, writing on Medium tells a story about how he nearly became the victim of bank fraud when he rented a property. After providing every possible kind of document to the real estate agent, including bank statements and investment portfolio details, he received a call from his bank a few days after he had moved in saying that someone wanted to cash a large check drawn on his account. Markovski knew he didn’t have that much money in is account, but the bank then told him that “he” had made a transfer from his savings account by phone. Of course, he’d made no such call, and thanks to his bank calling when they did, the theft was stopped. But, as he says, it was a “masterfully crafted plan that involved just four key steps”
1. Call the bank pretending to be Markovski
2. Change his phone number (to confirm large withdrawal)
3. Transfer all his savings into his current account
4. Have a fake cheque made and present it to the bank for withdrawal
They were able to do this because they had access to all the necessary information on him, including his social security number. They couldn’t catch the scammer, but it made Markovski think about why so much information was required to rent an apartment and why are we still relying on physical documents.
Blockchain has a solution — decentralised identity
Blockchain technology is opening up a range of possibilities to prevent this kind of crime and decentralised identity could be the way forward. As Markovski says, decentralised identity is “publicly discoverable identity information.” It uses blockchain technology to provide tamper-evident information about an entity or a subject and “allows a model of truth to be established between parties that rely on communication and exchange of data.”
There are already a few platforms working on this, including Civic, uPort and Sovrin. As Markovski says: “Decentralized identity platforms will change the current broken identity system that relies on numerous online services requiring us to remember passwords for each of them. They can help us protect our personal information and allow us to control how this data is shared.”
Until these platforms gain mass adoption — be careful out there!
Big data and data science are set to bring in a digital revolution with groundbreaking technologies like artificial intelligence (AI), machine learning (ML), and deep learning. The essence of data science is to dive into massive datasets to extract meaningful information from them. The insights that data scientists and data analysts obtain from large volumes of data is the secret sauce that’s rapidly transforming everything around us. Institutions and organizations across various sectors of the industry are now leveraging data science technologies to power innovation and technology-driven change. In fact, nearly 53 percent of companies have adopted big data analytics in 2017, which is an enormous growth from the 17 percent in 2015.
As more and more companies are inclining towards data science to transform their organizational infrastructure for the better, it is giving rise to new and exciting career opportunities such as data scientists, data analysts, ML engineers, data architects, big data engineers, and so on. Thus, if you wish to start a career in data science, the time is now. There are plenty of resources available today to help you get started in data science and online platforms offering specialized data science courses are an excellent option. The advantage of online courses is that you can master data science concepts at your own pace and convenience.
Data Science and Artificial Intelligence
The fields of data science, AI, and ML are intrinsically linked to one another. While artificial intelligence is a broad umbrella that includes a wide range of applications, right from text analysis to robotics, machine learning, is a subset of artificial intelligence that focuses on training machines how to ‘learn’ via advanced algorithms and perform specific tasks while simultaneously improving performance through experience. Data science is a branch of computer science that deals in the extraction of valuable insights from vast datasets through a combination of disciplines such as mathematics, ML, statistics, and data engineering.
Today, AI and ML technologies are transforming the industrial landscape, and this is possible only because these technologies are backed by data science. While AI is about creating “intelligent and smart” machines, it cannot do without ML. As mentioned before, machine learning algorithms are required to train machines to learn from behavior patterns and cues. Then again, ML cannot function without analytics, which in turn cannot function without data infrastructure. Harvard Business Review maintains that, “companies with strong basic analytics — such as sales data and market trends — make breakthroughs in complex and critical areas after layering in artificial intelligence.” However, for AI to create a true impact, you require the right data and a team of experienced and trained data science professionals who know where to look for the data and how to integrate it with AI and ML tools.
Let’s take the example of smart personal assistants like Siri, Alexa, and Cortana. These smart assistants represent the ‘Narrow AI’ and can interact with you and perform a limited number of tasks such as play songs for you, tell you about the day’s weather, or even do a little shopping for you. But, as we said, they can only perform ‘limited’ tasks since they have been exclusively ‘trained’ to do so. As data science continues to evolve in the future, data scientists might be able to tweak the algorithms of these assistants into more advanced ones (General AI) and then, maybe intelligent assistants can perform more complex tasks with much more precision than humans.
Data Science and Robotics
With the advance in data science, the field of robotics has definitely improved to a great extent. During the initial days of development, scientists were faced with two major challenges -one, predicting every action of a robot, and two, reducing the computational complexity in real-time vision tasks.
While robots could perform specific functions, it was impossible for scientists to predict their next move. For every new functionality, a robot would have to be reprogrammed every time, which made the task a tedious one. Another major obstacle with robots is that unlike humans who use their unique sense of vision to make sense of the world around them, robots can only visualize the world in a series of zeros and ones. Thus, accomplishing real-time vision tasks for robots would mean a fresh set of zeros and ones every time a new trend emerges, thereby increasing the computational complexity.
Enter machine learning to solve these issues in robotics. With ML, robots can acquire new behavior patterns through labeled data. Handwriting recognition is an excellent example. In handwriting recognition, computers are fed with labeled data — both positive and negative. Once the computer has successfully learned to differentiate between positive and negative examples, it is presented with new data. Based on the previous experience (during the training phase), the computer can predict the qualified classifiers for recognizing the handwriting. Thanks to advanced ML algorithms powered by tons of data that computers are now able to perform handwriting recognition much more accurately than they were ten years ago.
Furthermore, reinforcement learning, the branch of ML that is “the closest that machine learning can get to the way how humans learn” teaches computers and robots to perform specific functions according to their environment to generate outcomes that fetch either rewards or penalties. Thus, every time the robots lead to penalties, they can learn from their mistakes and know what course of action to take to fetch awards. Personalized recommendation lists of online portals such as Amazon and Netflix are the best examples of reinforcement learning. This wasn’t possible ten years ago!
As data scientists continue to leverage AI and ML to develop smart machines, in the process, they are gaining a deeper insight into the world of data science itself. Using AI and ML, data scientists and analysts can process, analyze, and interpret vast datasets much faster than ever. For instance, the MIT Data Science Machine can process large volumes of data and produce better predictive models anywhere between two to twelve hours, while the same would take months if done manually by data scientists. Another excellent case in point is that of California’s NuMedii Labs. Data scientists at the NuMedii Labs used network-based data mining algorithms to identify the correlations between the disease information and the drug composition to estimate the drug efficacy accurately. In this way, NuMedii aims to reduce the amount of time and risk associated with the process of drug development by bringing effective drugs into the market much faster than would happen through traditional methods.
Thus, data science, AI, and robotics have a pretty much symbiotic relationship. Each enhances the other to power innovative machines and technologies that are making our lives more convenient than ever. The collaboration between data science, AI, and ML has given us things like self-driving cars, smart assistants, robo-surgeons and nurses, and so much more. In the future, more is to come!
You may have seen a photo of a dress circulating on social media last year. People were asked what colour the dress was. Some saw white and gold, some lilac and gold, and others saw blue and black. The post demonstrated that perception is not universal, and the same can be said about cryptocurrency and blockchain technology, which can be viewed through two lenses.
Data and code first
There are those who perceive the technology to be the most important aspect of this new space and the one that will outlast all other aspects. Some people see the blockchain as a gigantic network of global computers working on the decentralised principle. Most often these ‘believers’ are software designers and developers who are focused on code and data. They see lots of potential in the blockchain for implementing new forms of software with new capabilities. It offers them data storage that is resistant to censorship and is immutable. It can also be audited and the code can’t be changed once it’s in use. This is one group, but there is another.
Another group perceives the technology as merely a tool that is necessary to create a new form of money. This group is more likely to be made up of people from backgrounds in economics and finance. They look at it from a perspective of the history of money and bring the idea that all forms of money have specific properties: resistant to forgery, secure, durable, measurable and divisible. So this group sees cryptocurrency as a new version of ‘sound money’. Some of them are sceptical about fiat currencies and aren’t fans of centrally controlled monetary policies. They see cryptocurrencies as a revolutionary new form of global money and the antidote to what they see as the questionable modern experiment of fiat currency.
The bigger picture
What does this leave us with? One group see crytpocurrency as the single useful purpose of the blockchain, while the other sees cryptocurrency as just one component in what the blockchain can do.
The ‘new money’ group actively buy cryptocurrencies and want to encourage mass adoption so the value of the coins increases. The blockchain-focused group is more enthusiastic about projects that experiment, add features, and explore the blockchain tradeoff-space.
But, what we can take away from this is that both views complement each other and one keeps the other in check. There is room for both perspectives and those working in the crypto space would do well to take a step back and take in a wider perspective that includes the views of both these groups to see exactly where the crypto space is heading.
The future we want is up to us and it’s time to take a side. Here, GQ explores the different ways artificial intelligence proves the best (and worst) is ours to control
Science-fiction guru William Gibson, who coined the term cyberspace, famously pointed out that the future is already here — it is just not very evenly distributed. We veer from anticipating a new dawn to prepping for the end of days. But the future we want is up to us and it’s time to take a side. Herewith, six digital advances that prove the best (and worst) is ours to control.
1. Beneficial artificial intelligence
Where we are: You can’t move for pundits telling us what artificial intelligence is going to do for us. It is transforming our world, but it has been for the past 50 years. Every smartphone is powered by AI research, giving us information retrieval via voice recognition or apps that spot our friends’ faces in the photos we take.
Where we’re headed: A world fuelled and enhanced by AI is one to look forward to. Autonomous cars will mean efficient and safe transport. Real-time translation buds that will enable you to speak one language and hear another will transform our travel experiences. Despite the cries of alarmists, there is little reason to believe that our AIs are going to “wake up” and decide to do away with us.
2. The datasphere
Where we are: Data changes everything: our personal lives, businesses and public services. One dramatic application is digitally powered precision medicine. Our bodies are constructed according to information encoded in our genes. Understanding how these instructions make proteins, build cells, repair damage and repel viruses is all driven by data.
Where we’re headed: New drugs, therapies and treatments will produce a revolution in the delivery of healthcare. What’s true for health is true for education, leisure, finance and travel. Every aspect of how individuals, corporations and governments function can be more effectively managed with the right application of the right data.
3. New companions
Where we are: They are already in our homes and in our lives, we know them as Alexa and Siri. These intelligent assistants will assume more and more of a role. As they learn from our interests and habits, they will become more informed of the information we need. They will not have any actual interest or awareness — but that won’t matter. We will increasingly treat them as our companions.
Where we’re headed: Humans will come to confide, trust and rely on our new companions. They will support us for better or worse, in our prime and our decline. Powered by AI and abundant data, they may assume the characteristics of those dear or near to you. Imagine your late grandmother or your favourite rock star chatting helpfully in your living room.
4. Unthinking artificial intelligence
Where we are: In the Terminator film series, Skynet, a defence network, suddenly becomes self-aware and launches a full-scale thermonuclear attack to get rid of humans. Nothing of that sort is about to happen. It is not AI we should fear but our own natural stupidity.
Where we’re headed: As we give systems control of our decision-making, we must not abdicate our responsibility. The danger is the unthinking digital system, without proper restraints, that launches an unthinking attack. We need to take great care whenever we take the human out of the decision loop when it comes to matters of life and death. AI systems lack moral sense and the broader contextual judgements of humans. A future of weaponised AI is one to fear.
5. Uncontrolled data analytics
Where we are: The widespread availability of high-quality data will be a boon. But data can be used to corrupt, misdirect and misinform. Recent events around Cambridge Analytica and its use of Facebook data to profile and target particular groups have caused a furore. Data used with the express aim of achieving a desired effect without the knowledge of the subjects themselves should make us very uneasy.
Where we’re headed: We have examples of data being used to make decisions on everything from prison sentences to credit ratings. The data can and often does encode bias. Courts in the US already use AI software to inform sentencing and the results are mixed. The AI notices the prejudices in its vast database of previous sentences and hands them down again as the usual “right” answer.
Algorithms can make decisions, but they can’t be accountable. Data collected from each of us every day and increasingly in the future can invade our privacy and reveal features of our lives that we ourselves are unaware of. Consumers and citizens should be empowered, not oppressed by data and its analysis.
6. Rampant cyber warfare
Where we are: Every second of every day computer networks around the world are under threat. Hackers and nation states all launch software to attack and subvert our digital systems. We are living in an age of increasingly frenzied but undeclared wars.
Where we’re headed: Because they are in virtual space, we don’t see the physical damage. But one day soon an airport will go dark or there will be a crash of the banking system. Countries, businesses and individuals are engaged in a digital arms race, desperately building new defences in the face of cyber attack. Computing is a dual-use technology. The same innovations that enhance our world can be used for harm. History points the way to a better future. For physical weapons, we have engineered and enforced conventions, treaties and limitations. We urgently need equivalents for our digital world.
Artificial intelligence key stats:
2.3m new jobs created by the AI industry by 2020
£5.4bn amount spent on AI by 2022
8m jobs lost as a result of the AI industry by 2020