Making the banking business even more difficult, smaller fintech and large techfin companies are developing solutions that use insight and digital technology to improve the customer experience across product lines. These new competitors threaten legacy financial institutions of all sizes. ... Failing to respond could lead to the demise of less agile organizations. The good news is that many of the new technologies that are threatening the banking industry also present significant opportunities. In fact, those organizations that can leverage big data, advanced analytics and new technologies to improve the customer experience can build trust, loyalty and revenues that are the keys to success in the future. According to Dan Cohen, Senior Vice President, Global Financial Services and Insurance at Atos, “Banks are at a crossroads. Continuous finTech innovation and new technologies such as blockchain are disrupting the market. While it creates threats, it also opens multiple opportunities for financial services to reinvent themselves and thrive.”
How automating feature engineering can help data scientists
Deep Feature Synthesis is an automated feature engineering approach that, essentially, can be applied to many different types of data, ranging from marketing use cases to financial services use cases to healthcare use cases. The general principle behind it is we're trying to emulate how human data scientists would approach these problems. Deep Feature Synthesis works by having a library of feature engineering building blocks called primitive functions, and each one of these primitives is labeled with the type of data it can input and the type of data it can output. To give you a very simple example, you can imagine a primitive that took in a list of numbers and outputted the maximum value in that list. We have a library of many of these primitives and when we get a new data set, Deep Feature Synthesis looks at the specific column and relationships in the data and figures out which primitives to apply. That's how it can take the generic primitives and create specific features.
Managing cloud infrastructure post-migration — a CTO guide
“This is something many businesses have quickly realised as they have continued along their deployment journeys. ...” “The skills gap has been an extremely prevalent issue in the cloud world for some time, with many businesses either lacking the budget to meet the substantial salaries that people with cloud skill sets now command, or simply unable to find people with the required level of technical expertise. This highlights the importance of finding the right partners so that businesses can hand off the most complicated jobs to a team of experts.” “However, it also highlights the need for better tooling for lifecycle management and operations. Lowering the barrier to entry, a solid choice of orchestration and management frameworks will take a pragmatic view on what’s needed to increase productivity around the day to day operations, and exceed expectations around even complicated processes such as upgrades of complex infrastructure software.”
Has storage become sexy?
The web-scale companies adopted this ethos with gusto and enterprise organizations soon began to follow suit. This march towards a commodity hardware dominated and software-driven world seemed inexorable. And then AI happened. Considering how long AI has been part of the public consciousness, it's almost funny that it snuck up on the entire tech industry. While industry leaders have been working on AI technologies for decades, until recently it didn't play a meaningful role in enterprise strategy nor was it a significant element of tech company go-to-market motions. And then, AI was everywhere. Because of its sudden rise as a top-of-mind issue, enterprise leaders were largely unprepared to deal with AI — and most critically, were ill-equipped to deal with the impact these new AI workloads would have on their newly cloudified architectures. As enterprises have begun working with AI, machine learning, advanced analytics, and other data- and resource-intensive workloads, they have found that commodity-based architectures built for traditional workloads buckle under the demands of these much more intense workloads.
Is Artificial Intelligence Dangerous? 6 AI Risks Everyone Should Know About
AI programmed to do something dangerous, as is the case with autonomous weapons programmed to kill, is one way AI can pose risks. It might even be plausible to expect that the nuclear arms race will be replaced with a global autonomous weapons race. Russia’s president Vladimir Putin said: “Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with enormous opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world.” Aside from being concerned that autonomous weapons might gain a “mind of their own,” a more imminent concern is the dangers autonomous weapons might have with an individual or government that doesn’t value human life. Once deployed, they will likely be difficult to dismantle or combat.
Code First: Girls teaches more women to code in UK than universities
“We are working very closely with, for example, the Institute of Coding,” she said. “We are very much working together to try and address this challenge because they also acknowledge that these numbers just aren’t good enough.” The social enterprise has announced a partnership with telecoms and broadband provider BT to teach cohorts of 30 women the skills they need for a job in tech in a free four-month course. The programme will teach women skills such as web development, Python programming, databases, test-driven development, agile development and cyber security, and participants will be given the opportunity to be interviewed for a job in a BT tech team. De Alwis said BT approached Code First: Girls to ask for help in training groups of women with the potential goal of hiring them, and she pointed out that the organisation helps companies feel confident in hiring outside their usual talent pool.
A closer look at HTC’s blockchain phone, the Exodus 1
The future of all of this is still very much up in the air. “I see us as the trusted Android,” Chen says, vaguely alluding to a future road map that finds HTC shifting its focus from hardware to software and IP. “We’re not talking about [monetization] right now, but we have some ideas.” While the devoted blockchain phone is largely a stepping stone toward incorporating that technology into more mainstream devices, there are plans to continue development on the line, as the Exodus 1 name optimistically implies. Chen explains that the company is working on follows that will be further distinguished from other handsets, though he’s not ready to discuss specifics. Presently HTC has between 20 and 30 engineers working on the blockchain project, bringing in expert in the space to educate them on the intricacies of the technologies. Event among those who are currently devoted to building out the device, this is all clearly very much a learning process.
The actual cost of downtime in the manufacturing industry
Of course, while gathering data is a key driver in solving problems and having a better understanding of downtime, just obtaining more data does not mean that an organization will know what to do with it. According to a recent study by Accenture, 60% of operators cite dealing with outcomes of data gathered as a major challenge. It is important to understand the reasons for collecting increasing amounts of data and how the data can be applied to improve condition-based monitoring and predictive maintenance, including: The ability to identify data-based patterns; Cognitive learning capabilities; Opportunities to leverage data in the Cloud for cross-organization/industry comparisons; and The ability to share data with trusted service providers for additional analysis and insights There is a significant opportunity to continuing carving down unplanned downtime through digitization, but as Deloitte notes in a recent report, “Simply ‘doing’ digital things will not make an organization digital.” Organizations need to go beyond just technology changes to truly embrace the benefits of digitization.
Supporting Multiple API Protocols with Thriftly
Bitfire Safety is a fictional fire protection company. Bitfire Safety provides dry pipe sprinkler system installations for customers that own cold-climate structures, such as parking garages. These systems are installed and configured with a command panel system interface and software that is used to locally monitor and test various aspects of the system. As part of a modernization initiative, Bitfire Safety is enhancing their services to include remote monitoring and issue remediation. They are first concentrating on the monitoring of the supervisory pressure switches. These switches are responsible for ensuring the proper system pressure and will pump or release pressure through a ball valve to maintain the correct levels. Through monitoring, Bitfire Safety can identify when pressures are tracking low or high. Low pressures could be indicative of an air compressor failing or a leak in the system; pressures tracking high could lead to damaging clappers and gaskets in the system, and could pose a safety risk in the event of a fire where open clappers would just bleed off system air rather than delivering water to a fire.
Building Human Interfaces With Artificial Intelligence
The main trick here is to allow humans to stay human. For decades computers were not exciting to use as they required us to change our ways. We needed to click the right buttons, in the right order to achieve a task. We needed to remember passwords and addresses and know which program to use for different tasks. In essence, we needed to get conditioned to software to use it and to learn how to interface with it before we enjoyed it. When you talk to Cortana, Siri or Google, you don’t need to use a keyboard or a mouse and you can ask questions like "what is the temperature today in the capital of Denmark?" without having to know what the capital is or tell the computer what "today" means. We have a lot of data already out there and computers can analyse the data without extra work from our side. That way we add the extra information the computer needs to give us the right results for the questions we ask.
Quote for the day:
"The final test of a leader is that he leaves behind him in other men, the conviction and the will to carry on." -- Walter Lippmann
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