Six cyber security startups kick off with CyLon accelerator
CyLon is supported by sponsorship from technology defence and security specialist Raytheon. CEO of Cyberlytic Stuart Laidlaw said his team – one of the selected six teams to form the first cohort – are looking forward to starting at CyLon. “We are delighted to have been selected for the first CyLon programme, which offers us a fantastic platform to grow our business into a leading global cyber security provider," he said. According to Iain Lobban, director of GCHQ until November 2014, cyber security is one of the most challenging issues in this generation.
Data science demands elastic infrastructure
The problem with trying to run big data projects within a data center revolves around rigidity. As Matt Wood told me in a recent interview, this problem "is not so much about absolute scale of data but rather relative scale of data." ... In a separate conversation, he elaborates: "Those that go out and buy expensive infrastructure find that the problem scope and domain shift really quickly. By the time they get around to answering the original question, the business has moved on. You need an environment that is flexible and allows you to quickly respond to changing big data requirements. Your resource mix is continually evolving--if you buy infrastructure, it's almost immediately irrelevant to your business because it's frozen in time. It's solving a problem you may not have or care about any more."
Anticipating the digital future
AI will more aggressively support decision making. The resulting information will be presented in a way that it can be absorbed through multiple senses. OK, that’s new. Privacy will increasingly be a problem/opportunity and while this will likely vary greatly across age groups, consumer-directed tools should help close the gap on privacy fairness. To net out much of this, the future will require a vastly changed set of tools and skills and only by focusing on remaining agile and keeping your eye on the trends, problems, and related technology advancements will you have a hope of keeping up. Good news is that most clearly won’t be able to so if you can keep up you’ll stand out sharply in a crowd of under performers.
The Non-parametric Bootstrap as a Bayesian Model
Still, the bootstrap produces something that looks very much like draws from a posterior and there are papers comparing the bootstrap to Bayesian models (for example, Alfaro et al., 2003). Some also wonder which alternative is more appropriate: Bayes or bootstrap? But these are not opposing alternatives, becausethe non-parametric bootstrap is a Bayesian model. In this post I will show how the classical non-parametric bootstrap of Efron (1979) can be viewed as a Bayesian model. I will start by introducing the so-called Bayesian bootstrap and then I will show three ways the classical bootstrap can be considered a special case of the Bayesian bootstrap. So basically this post is just a rehash of Rubin’s The Bayesian Bootstrap from 1981.
5 Things To Know About The Rise Of Open Source
If you still think open source technology is less reliable than proprietary software, or less secure, it’s time to learn more about the private sector’s digital revolution. During the past year major tech brands such as Google, Facebook and Microsoft have adopted a more open source philosophy, evident in their latest software releases. Similarly, more large companies are utilizing open source solutions alongside proprietary software to tap into open source’s diverse, creative, cooperative community of developers, thought leaders and users. If you want to expand the use of open source in your own business, there are a few things you should know.
What’s slowing down your network and how to fix it
The all-too-obvious answer is to see bandwidth as the problem, but with investigation, it is often not within a LAN environment, where a high amount of bandwidth is available. More likely, the problem lies within the WAN, where capacity is more finite and expensive. Problems with slow networks in a WAN environment are more likely to result from not employing quality-of-service software, according to Jason Peach, principal consultant at Networks First. “Rather than throwing more bandwidth at the problem, using more intelligent analysis to optimise bandwidth is often a better way to solve a bandwidth contention – the problem in any network scenario – LAN, WAN or WLAN, for example,” he says.
How wearables and mobile health tech are reshaping clinical trials
The average cost of bringing a drug from development to FDA approval is over $2.5 billion, according to a recent study by The Tufts Center for the Study of Drug Development. This figure includes costs for the drugs that don’t make it through to the approval phase, and the Tufts Center notes that higher drug failure rates contribute significantly to increases in R&D costs. But there’s a big opportunity here: If life science companies can get enough insight early in development, they can create a more efficient drug development process and prioritize resources for the most promising therapies. Big data analytics and new clinical technology — such as mobile health solutions and wearable devices — promise to significantly change how trials are conducted and increase the value of the data and insights that come out of these trials.
Hollywood movies vs. the real future of AI
AI is a supremely complex technology to understand, let alone create, and oftentimes Hollywood blockbusters stretch the technology's limitations to fit some desired scenario. In other words, the AI popularized and propagated by Hollywood seldom reflects the direction the technology is actually headed. "AI is nowhere near able to take over the world in the next few years," said Charlie Ortiz, senior principal manager of the Artificial Intelligence and Reasoning Group within Nuance's Natural Language and AI Laboratory. "And given the distance to that point, there are lots of other futures that could evolve. It could very well evolve into something that is more helpful and collaborative and could teach us if necessary."
Designing an Impediment Removal Process for Your Organization
Instead of trying to find and eliminate waste as a means of improving efficiency, I find it more natural to focus on the flow of work as a means of improving effectiveness. From that perspective, two questions become central. The first questions is “how does work flow through our system”? It can be very revealing for people to see the end-to-end picture of how work flows through the entire system, and not just their nominal area of functional responsibility. Managers and leaders from across the organization need to work together to create this picture. The second question is “what impedes the flow of work through the system”? Or, asked a different way, “what opportunities exist to improve the flow of work through the system?”
John Zachman on gaining synergies among the major EA frameworks
Friends of mine wanted me to change the name of this to Zachman Ontology, because if you recognize this, this is not a methodology; this is an ontology. This does not say anything about how you do Enterprise Architecture—top-down, bottom-up, left to right, right to left, where it starts. It says nothing about how you create it. This just says this is a total set of descriptive representations that are relevant for describing a complex object. ... A framework is a structure. A structure defines something. In contrast, a methodology is a process, a process to transform something. And a structure is not a process, and a process is not a structure. You have two different things going on here.
Quote for the day:
"If you genuinely want something, don't wait for it--teach yourself to be impatient." -- Gurbaksh Chahal
CyLon is supported by sponsorship from technology defence and security specialist Raytheon. CEO of Cyberlytic Stuart Laidlaw said his team – one of the selected six teams to form the first cohort – are looking forward to starting at CyLon. “We are delighted to have been selected for the first CyLon programme, which offers us a fantastic platform to grow our business into a leading global cyber security provider," he said. According to Iain Lobban, director of GCHQ until November 2014, cyber security is one of the most challenging issues in this generation.
Data science demands elastic infrastructure
The problem with trying to run big data projects within a data center revolves around rigidity. As Matt Wood told me in a recent interview, this problem "is not so much about absolute scale of data but rather relative scale of data." ... In a separate conversation, he elaborates: "Those that go out and buy expensive infrastructure find that the problem scope and domain shift really quickly. By the time they get around to answering the original question, the business has moved on. You need an environment that is flexible and allows you to quickly respond to changing big data requirements. Your resource mix is continually evolving--if you buy infrastructure, it's almost immediately irrelevant to your business because it's frozen in time. It's solving a problem you may not have or care about any more."
Anticipating the digital future
AI will more aggressively support decision making. The resulting information will be presented in a way that it can be absorbed through multiple senses. OK, that’s new. Privacy will increasingly be a problem/opportunity and while this will likely vary greatly across age groups, consumer-directed tools should help close the gap on privacy fairness. To net out much of this, the future will require a vastly changed set of tools and skills and only by focusing on remaining agile and keeping your eye on the trends, problems, and related technology advancements will you have a hope of keeping up. Good news is that most clearly won’t be able to so if you can keep up you’ll stand out sharply in a crowd of under performers.
The Non-parametric Bootstrap as a Bayesian Model
Still, the bootstrap produces something that looks very much like draws from a posterior and there are papers comparing the bootstrap to Bayesian models (for example, Alfaro et al., 2003). Some also wonder which alternative is more appropriate: Bayes or bootstrap? But these are not opposing alternatives, becausethe non-parametric bootstrap is a Bayesian model. In this post I will show how the classical non-parametric bootstrap of Efron (1979) can be viewed as a Bayesian model. I will start by introducing the so-called Bayesian bootstrap and then I will show three ways the classical bootstrap can be considered a special case of the Bayesian bootstrap. So basically this post is just a rehash of Rubin’s The Bayesian Bootstrap from 1981.
5 Things To Know About The Rise Of Open Source
If you still think open source technology is less reliable than proprietary software, or less secure, it’s time to learn more about the private sector’s digital revolution. During the past year major tech brands such as Google, Facebook and Microsoft have adopted a more open source philosophy, evident in their latest software releases. Similarly, more large companies are utilizing open source solutions alongside proprietary software to tap into open source’s diverse, creative, cooperative community of developers, thought leaders and users. If you want to expand the use of open source in your own business, there are a few things you should know.
What’s slowing down your network and how to fix it
The all-too-obvious answer is to see bandwidth as the problem, but with investigation, it is often not within a LAN environment, where a high amount of bandwidth is available. More likely, the problem lies within the WAN, where capacity is more finite and expensive. Problems with slow networks in a WAN environment are more likely to result from not employing quality-of-service software, according to Jason Peach, principal consultant at Networks First. “Rather than throwing more bandwidth at the problem, using more intelligent analysis to optimise bandwidth is often a better way to solve a bandwidth contention – the problem in any network scenario – LAN, WAN or WLAN, for example,” he says.
How wearables and mobile health tech are reshaping clinical trials
The average cost of bringing a drug from development to FDA approval is over $2.5 billion, according to a recent study by The Tufts Center for the Study of Drug Development. This figure includes costs for the drugs that don’t make it through to the approval phase, and the Tufts Center notes that higher drug failure rates contribute significantly to increases in R&D costs. But there’s a big opportunity here: If life science companies can get enough insight early in development, they can create a more efficient drug development process and prioritize resources for the most promising therapies. Big data analytics and new clinical technology — such as mobile health solutions and wearable devices — promise to significantly change how trials are conducted and increase the value of the data and insights that come out of these trials.
Hollywood movies vs. the real future of AI
AI is a supremely complex technology to understand, let alone create, and oftentimes Hollywood blockbusters stretch the technology's limitations to fit some desired scenario. In other words, the AI popularized and propagated by Hollywood seldom reflects the direction the technology is actually headed. "AI is nowhere near able to take over the world in the next few years," said Charlie Ortiz, senior principal manager of the Artificial Intelligence and Reasoning Group within Nuance's Natural Language and AI Laboratory. "And given the distance to that point, there are lots of other futures that could evolve. It could very well evolve into something that is more helpful and collaborative and could teach us if necessary."
Designing an Impediment Removal Process for Your Organization
Instead of trying to find and eliminate waste as a means of improving efficiency, I find it more natural to focus on the flow of work as a means of improving effectiveness. From that perspective, two questions become central. The first questions is “how does work flow through our system”? It can be very revealing for people to see the end-to-end picture of how work flows through the entire system, and not just their nominal area of functional responsibility. Managers and leaders from across the organization need to work together to create this picture. The second question is “what impedes the flow of work through the system”? Or, asked a different way, “what opportunities exist to improve the flow of work through the system?”
John Zachman on gaining synergies among the major EA frameworks
Friends of mine wanted me to change the name of this to Zachman Ontology, because if you recognize this, this is not a methodology; this is an ontology. This does not say anything about how you do Enterprise Architecture—top-down, bottom-up, left to right, right to left, where it starts. It says nothing about how you create it. This just says this is a total set of descriptive representations that are relevant for describing a complex object. ... A framework is a structure. A structure defines something. In contrast, a methodology is a process, a process to transform something. And a structure is not a process, and a process is not a structure. You have two different things going on here.
Quote for the day:
"If you genuinely want something, don't wait for it--teach yourself to be impatient." -- Gurbaksh Chahal
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