For some Internet of Things devices, like connected video cameras, it also ceases to be practical to send the data to the cloud just because of the pure volume involved. As an example, he points out that there are already a half a billion connected cameras in place today with a billion expected to be deployed worldwide by 2020. As he says, once you get over 1080p quality, it really ceases to make sense to send the video to the cloud for processing, at least initially, especially if you are using the cameras in a sensitive security zone like an airport where you need to make decisions fast if there is an issue. Then there’s latency. Talla echoes Levine’s thinking here, saying machines like self-driving cars and industrial robots need decisions in fractions of seconds, and there just isn’t time to send the data to the cloud and back.
The fact that government authorities have become so active in blockchain experimentation means that we will soon reach a point where corporations will be expected to interact with those authorities using the distributed ledger technology. Whether through basic blockchain-based registration processes or more elaborate data-transfer protocols, corporations need to start thinking about whether or not their existing technologies and ERP systems can support this type of dialogue. From a financial perspective, the C-suite also needs to think about the competition. Ultimately, the goal of blockchain is to reduce administrative costs. Companies that get out ahead, finding ways to practically leverage the blockchain to make their operations more efficient, will have an edge on their competition when it comes to annual operating costs.
Just as any other department uses operational metrics to measure the efficiency and effectiveness of their organizations, data science needs to do the same. Sales teams use funnel metrics to measure how effectively their teams are converting prospects down the funnel to closed-won. Engineering teams use sprint burndown and team velocity to measure how efficiently their teams complete work in a given amount of time. Monitoring leading indicators allow these teams to quickly adjust before a revenue number is missed or a product feature is delayed. For data science teams, it is important to identify critical information like when a data scientist is spending precious hours reproducing something someone else has already done. Or when a business stakeholder has not been looped in to give feedback on a project before it is delivered as “done”.
Large commercial banks have several back-office processes that AI’s operating models are transforming. Operations such as reconciliation, consolidation and credit risk management, can be completed with robotic process automation (RPA), which along with machine learning can be fully automated. Banking functions that are central to operations such as the quarterly closing and reporting of earnings can be accomplished in real time with AI, allowing for greater accuracy and quicker adjustments. A case in point is the Machine Learning program called COIN used by JPMorgan Chase & Co, one of the biggest banks in the United States. The AI program takes just a few seconds to review commercial loan agreements as compared to the 360,000 hours each year that a team of lawyers and loan officers would take to complete.
The technology is still immature and likely to transition quickly, and fierce competition from rival distributed ledger platforms is a threat. Moreover, the computing power needed to achieve consensus of distributed transactions is expensive, and more cost-efficient models need to be found. With the likes of BP and Microsoft opting for ethereum, there also needs to be more standardised regulation and stability of the cryptocurrencies so larger enterprises can comfortably roll out the technology for customer use. Blockchains may be able to provide an additional layer of security and operational efficiency to existing IoT models, but the technology is not necessarily needed in every use case. In fact, there are IoT platforms already in place that implement supply chain traceability, monitor product provenance, and ensure product authenticity.
“If we’re lucky, they’ll treat us as pets,” says Paul Saffo, a consulting professor at Stanford University, “and if we’re very unlucky, they’ll treat us as food.” But there are academics, including Noam Chomsky, who don’t believe computers will ever be able to attain that level of intelligence. Yes, they might be able to learn to speak Chinese, but will they ever truly understand Chinese, or merely simulate understanding? These questions are all bound up with concepts of intelligence, sentience, self-awareness and consciousness, things that remain stubbornly impervious to scientific analysis. Zuckerberg’s angle on AI, shared by other industry figures such as Google’s Ray Kurzweil, is that super-smart micro-intelligences offer great benefit to mankind and will always remain under our ultimate control.
The world is connected through the internet now; it is at everyone’s fingertips. Computers make us more efficient at what we do than we ever were. What used to take us days takes minutes now. We couldn’t make more time, so instead we make more out of the time we have. Computing power is such an integral part of our life that it has become as scarce and valuable as gold was to our ancestors. And when smart people asked the profound question: “Can we make a currency that is backed by computing power?,” the answer was cryptocurrencies and blockchains. If you don’t yet understand how blockchains work, I’ve written the ultimate guide in plain English to help you understand the concept.
Not surprisingly, software engineering teams are generally not well-equipped to handle these complexities and so can fail pretty seriously. “A good, solid, and comprehensive platform that lets you scale effortlessly is a critical component” to overcoming some of this complexity, Dunning says. “You need to focus maniacally on establishing value for your customers and you can't do that if you don't get a platform that has all the capabilities you need and that will allow you to focus on the data in your life and how that is going to lead to customer-perceived value.” Enter TensorFlow. Open source, a common currency for developers, has taken on a more important role in big data. Even so, Dunning asserts that “open source projects have never really been on the leading edge of production machine learning until quite recently.”
Social engineering has proven to be a very successful way for a criminal to "get inside" your organization. Once a social engineer has a trusted employee's password, he can simply log in and snoop around for sensitive data. With an access card or code in order to physically get inside a facility, the criminal can access data, steal assets or even harm people. Chris Nickerson, founder of Lares, a Colorado-based security consultancy, conducts 'red team testing' for clients using social engineering techniques to see where a company is vulnerable. Nickerson detailed for CSO how easy it is to get inside a building without question. In one penetration test, Nickerson used current events, public information available on social network sites, and a $4 Cisco shirt he purchased at a thrift store to prepare for his illegal entry.
If you're working in a workbook you've saved in OneDrive or SharePoint, you'll see a new button on the Ribbon, just to the right of the Share button. It's the Activity button, and it's particularly handy for shared workbooks. Click it and you'll see the history of what's been done to the spreadsheet, notably who has saved it and when. To see a previous version, click the "Open version" link underneath when someone has saved it, and the older version will appear. And there's a very useful difference in what Microsoft calls the backstage area that appears when you click File on the Ribbon: If you click Open, Save or Save As from the menu on the left, you can see the cloud-based services you've connected to your Office account, such as SharePoint and OneDrive. Each location now displays its associated email address underneath it.
The 306 million passwords encompass more than 1 billion compromised accounts. The data comes from rich sources, including the Exploit.in and Anti-Public lists. Both of those lists were massive mash-ups of stolen data covering just over 1 billion email addresses. Hunt calls the service Pwned Passwords. Service providers can use the data in their back-end systems with the aim of improving the state of password security. Hunt has made a 6GB file available with the data. For example, if someone is registering a new account, a service provider can compare the chosen password and warn the individual that the password has been compromised before. At that point, the person can be strongly encouraged or forced to choose a more secure password.
Quote for the day:
"Strategy is not really a solo sport even if you're the CEO." -- Max McKeown