September 19, 2020
Why we need XAI, not just responsible AI
There are many techniques organisations can use to develop XAI. As well as continually teaching their system new things, they need to ensure that it is learning correct information and does not use one mistake or piece of biased information as the basis for all future analysis. Multilingual semantic searches are vital, particularly for unstructured information. They can filter out the white noise and minimise the risk of seeing the same risk or opportunity multiple times. Organisations should also add a human element to their AI, particularly if building a watch list. If a system automatically red flags criminal convictions without scoring them for severity, a person with a speeding fine could be treated in the same way as one serving a long prison sentence. For XAI, systems should always err on the side of the positive. If a red flag is raised, the AI system should not give a flat ‘no’ but should raise an alert for checking by a human. Finally, even the best AI system should generate a few mistakes. Performance should be an eight out of ten, never a ten, or it becomes impossible to trust that the system is working properly. Mistakes can be addressed, and performance continually tweaked, but there will never be perfection.
What classic software developers need to know about quantum computing
There are many different parts of quantum that are exciting to study. One is quantum computing using quantum to do any sort of information processing, the other is communication itself. And maybe the third part that doesn't get as much media attention but should is sensing, using quantum computers to sense things much more sensitively than you would classically. So think about sensing very small magnetic fields for example. So the communication aspect of it is just as important because at the end of the day it's important to have secure communication between your quantum computers as well. So this is something exciting to look forward to. ... So the first tool that you need, and one of the most important tools is the one that gives you access to the quantum computers. So if you go to quantum-computing.ibm.com and create an account there, we give you immediate access to several quantum computers, which first of all, every time I say, this just blows my mind because four years ago this wasn't a thing. You couldn't go online and access a quantum computer. I was in grad school because I wanted to do quantum research and needed access to a lab to do this work
Why Darknet Markets Persist
"There are two main reasons here: the lack of alternatives and the ease of use of marketplaces," researchers at the Photon Research Team at digital risk protection firm Digital Shadows tell Information Security Media Group. At least for English-speaking users, such considerations often appear to trump other options, which include encrypted messaging apps as well as forums devoted to cybercrime or hacking. And many users continue to rely on markets despite the threat of exit scams, getting scammed by sellers or getting identified and arrested by police. Another option is Russian-language cybercrime forums, which continue to thrive, with many hosting high-value items. But researchers say that, even when armed with translation software, English speakers often have difficulty coping with Russian cybercrime argot. Many Russian speakers also refuse to do business with anyone from the West. ... Demand for new English-language cybercrime markets continues to be high because so many existing markets get disrupted by law enforcement agencies or have administrators who run an exit scam. Before Empire, other markets that closed after their admins "exit scammed" have included BitBazaar in August, Apollon in March and Nightmare in August 2019.
Open Data Institute explores diverse range of data governance structures
The involvement of different kinds of stakeholders in any particular institution also has an effect on what kinds of governance structures would be appropriate, as different incentives are needed to motivate different actors to behave as responsible and ethical stewards of the data. In the context of the private sector, for example, enterprises that would normally adopt a cut-throat, competitive mindset need to be incentivised for collaboration. Meanwhile, cash-strapped third-sector organisations, such as charities and non-governmental organisations (NGOs), need more financial backing to realise the potential benefits of data institutions. “Many [private sector] organisations are well-versed in stewarding data for their own benefit, so part of the challenge here is for existing data institutions in the private sector to steward it in ways that unlock value for other actors, whether that’s economic value from say a competition point of view, but then also from a societal point of view,” said Hardinges. “Getting organisations to consider themselves data institutions, and in ways that unlock public value from private data, is a really important part of it.”
5 supply chain cybersecurity risks and best practices
Falling prey to the "it couldn't happen to us" mentality is a big mistake. But despite clear evidence that supply chain cyber attacks are on the rise, some leaders aren't facing that reality, even if they do understand techniques to build supply chain resilience more broadly. One of the biggest supply chain challenges is leaders thinking they're not going to be hacked, said Jorge Rey, the principal in charge of information security and compliance for services at Kaufman Rossin, a CPA and advisory firm in Miami. To fully address supply chain cybersecurity, supply chain leaders must realize they need to face the risk reality. The supply chain is veritable smorgasbord of exploit opportunities -- there are so many information and product handoffs in even a simpler one -- and each handoff represents risks, especially where digital technology is involved but easily overlooked. ... Supply chain cyber attacks are carried out with different goals in mind -- from ransom to sabotage to theft of intellectual property, Atwood said. These cyberattacks can also take many forms, such as hijacking software updates and injecting malicious code into legitimate software, as well as targeting IT and operational technology and hitting every domain and any node, Atwood said.
Moving Toward Smarter Data: Graph Databases and Machine Learning
Data plays a significant role in machine learning, and formatting it in ways that a machine learning algorithm can train on is imperative. Data pipelines were created to address this. A data pipeline is a process through which raw data is extracted from the database (or other data sources), is transformed, and is then loaded into a form that a machine learning algorithm can train and test on. Connected features are those features that are inherent in the topology of the graph. For example, how many edges (i.e., relationships) to other nodes does a specific node have? If many nodes are close together in the graph, a community of nodes may exist there. Some nodes will be part of that community while others may not. If a specific node has many outgoing relationships, that node’s influence on other nodes could be higher, given the right domain and context. Like other features being extracted from the data and used for training and testing, connected features can be extracted by doing a custom query based on the understanding of the problem space. However, given that these patterns can be generalized for all graphs, unsupervised algorithms have been created that extract key information about the topology of your graph data and used as features for training your model.
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