We’ve already seen some of the threats manifest themselves in various ways, including biased algorithms, AI-based forgery and the spread of fake news during important events such as elections. The past few years have seen the development of a growing discussion around building trust in artificial intelligence and creating safeguards that prevent abuse and malicious behavior of AI models. The various efforts are focused in three fields of fairness, explainability and robustness. It’s important to create robust AI models and to evaluate the resilience of artificial intelligence algorithms against abuse and erratic behavior.
As AI gets better and more sophisticated, it also enables cybercriminals to use deep learning and AI to breach security systems just as cybersecurity experts use the same technology tools to detect suspicious online behavior. Deepfakes, using AI to superimpose one person's face or voice over another in a video, for example, and other advanced AI-based methods will probably play a larger role in social media cybercrime and social engineering. It sounds scary, and it's not science fiction.
Today, when customers seek instant responses to their queries, chatbots are a popular solution. They are already making their way to messaging mediums. The real challenge, though, lies in building a chatbot that is engaging, interactive and is able to provide real value to users. With easy-to-use chatbot platforms, making a chatbot is now quick and hassle-free. And with unprecedented technological advancements in artificial intelligence and machine learning, the future of chatbots looks bright.
Recall that a Docker image is made of a Dockerfile + any necessary dependencies. Also recall that a Docker container is a Docker image brought to life. To work with Docker commands, you first need to know whether you’re dealing with an image or a container. Once you know what you’re working with you can find the right command for the job. This article highlights the key commands for running vanilla Docker. Here are a few things to know about Docker commands.
You could say there is too much AI hype. It is not a contentious thing to say, it just is. Maybe it would help if we re-defined it. Instead is saying AI means artificial intelligence, maybe we should return to an earlier definition, algorithmic intelligence, instead. Artificial intelligence is the application of algorithmic computation to large data sets. To see through the hype, just remember that AI could just as easily mean algorithmic intelligence.
HR representatives can dig into data to tackle their needs. Doing so can help their companies and employees thrive in an ever-demanding landscape. HR data can help companies figure out what factors make employees most likely to quit. It can also pinpoint which workers might be feeling upset about their roles, allowing HR representatives to intervene proactively. HR people often use technologies to impact their work, big data among them. Here are six ways big data in HR has had an impact.
If robots are truly taking over, then why are having trouble finding enough humans to do work that needs being done? The truth is that automation doesn’t replace jobs, it replaces tasks and when tasks become automated, they largely become commoditized. So while there are significant causes for concern about automation, such as increasing returns to capital amid decreasing returns to labor, the real danger isn’t with automation itself, but what we choose to do with it.
By enabling the digitization of assets, blockchain technology is driving a fundamental shift from the Internet of information, where we can instantly view, exchange and communicate information to the Internet of value, where we can instantly exchange assets. A new global economy of immediate value transfer is on its way, where big intermediaries no longer play a major role. Blockchain will profoundly disrupt hundreds of industries that rely on intermediaries, including banking, finance, academia, real estate, insurance, legal, healthcare and the public sector — amongst many others.
Today’s consumers are digital natives who are media-savvy, and that’s made them impervious to conventional marketing techniques. Rather than just trying to sell them something, businesses need to find a way to reach them on a deeper, more personal level. And it’s easy to understand why technology has taken on an increasingly prominent role in this sector in recent years, forcing companies to adapt or risk being left behind. In fact, it’s increasingly obvious that tech is the future of marketing.
In the past few years, telepresence robots have been around, but have not gained widespread traction in the enterprise. Yet, increasingly we are seeing classes of robots designed to interact with people in a distributed physical space. Moving into 2019, we will see an expansion of these office and personal robots meant to help us collaborate and coordinate action with others. As the year comes to an end, here are some predictions on digital workplace, specifically, the rise of the cobot - a robot interacting and functioning with people.
Digital transformation is bringing fundamental change to businesses across all sectors. Business leaders and IT professionals know tsunami of data is here, but many don’t yet have a comprehensive strategy to integrate it all, so they rely on stop-gap measures. Four data myths in particular hold companies back from creating an effective, long-term data integration strategy. Here’s a brief overview of each myth — and an explanation of why it’s leading businesses astray.
Despite chatbots sometimes being regarded as an overhyped technology, most realistic scenarios indicate that the bots will inevitably take on the role of being the first line of technical support. By its very essence, this technology opens up access to the new market opportunities by encouraging scalable, one-on-one conversations between brands and consumers. Creating a world where companies are building stronger relationships with their clientele is definitely worth it. There is no doubt that in the foreseeable future, chatbots will ultimately become what they had been designed to - an irreplaceable assistant for humanity.
The blockchain value framework is aimed at helping organizations identify the concrete value of blockchain technology in their use-case proposals and build a corresponding business case. The framework has three distinct dimensions: improved productivity and quality, increased transparency among parties, and reinventing products and processes. Each dimension includes a distinct set of blockchain-enabling capabilities that provide a solution to a concrete pain point or present an area of opportunity. In addition, the framework will help identify where the real value will be created.
Facial recognition holds so much more potential. Massive opportunity lies in facial recognition software, from helping to identify missing or exploited children to speeding up lines in the airport. As marketers trying to leverage facial recognition as a vehicle to improving customer experience, is there still a way for us to use the technology? How can we tap into the benefits without crossing the line of personal privacy? Before the technology is unleashed into the corporate world, companies need to fully understand it.
Marketers use big data to sell more products. Big data marketing, however, analysing data to improve marketing activities isn’t about more ads. It’s about serving the right ads to the right people at the right time. It’s about answering questions regarding what type of message resonates with customers, which landing page is the most efficient, or which social media platform should the company use to reach their target audience. Without big data, companies operate on unproven assumptions, which is never a good idea. That’s why businesses use big data marketing to solve puzzling issues.
Open source is a key enabler for enterprise data science, both in terms of the growing ecosystem of open-source tools and the expanding number of complementary enterprise data science platforms that incorporate and build on open source languages and tools. The challenge is identifying which of those tools is relevant and valuable to your business. Assessing the maturity of these projects, grappling with any licensing issues and making sure your team has the correct skillset to use them are challenges that many companies are now facing.
Random search is a really useful tool in a data scientist toolbox. It’s a very simple technique and can be a powerful tool to perform feature selection. It’s not meant to give the reasons why some features are more useful than other ones (as opposed to other feature selection procedures like Recursive Feature Elimination), but it can be a useful tool to reach good results in less time. Learn how to use a simple random search in Python to get good results in less time.
The Internet of Things technology provides a lot of opportunities for the education industry. Of course, the primary purpose of integrating IoT is making the learning and teaching processes easier and faster. This technology offers students and teachers various ways of communication, along with capabilities to share and edit learning materials. The education field has some issues that IoT is able to solve. In this article, we’ll discuss five solutions that can improve the learning and teaching processes along with successful projects.
It is found that half of the recruiters, HR representatives and CEOs have already recognized and anticipated the future of HR analytics. Artificial intelligence can disrupt and transform the future of HR analytics and HR operations, but the organizations will have to balance out these cognitive computing functions and advancements with complete transparency in order to implement AI successfully. Moreover, transparency in AI technology is important for employees to trust new technology. HR executives must have a clear understanding of how decisions are being made.
The future of finance is bound to be shaped by emerging technologies such as artificial intelligence (AI) and blockchain. Algorithms will play a major role in the industry, spotting money laundering schemes and helping banks better assess the creditworthiness of potential clients. Blockchain might power a real-time, cross-border payment system that’s faster and cheaper than traditional options. Winning tomorrow’s customers will depend on financial companies merging various technologies and services into a single experience that attracts and retains even the most demanding customers.
Mobile apps and Big Data have already revolutionized the world, to a great extent. The healthcare industry is no exception to this. The two big technologies, individually and combined, are sure to make healthcare cost-effective, accessible, and all the way more innovative. The goal is to not only get efficient treatments but also to need them a little less. Physicians believe that mobile health apps can improve patients’ health, and find value having a mobile health app connected to Emergency Health Services. Here are 5 ways Mobile apps and Big Data is improving Healthcare
One of the main tasks that a data scientist must face when he builds a machine learning model is the selection of the most predictive variables. Selecting predictors with low predictive power can lead, in fact, to overfitting or low model performance. This article shows you some techniques to better select the predictors of a dataset in a binary classification model, and two simple techniques in R to measure the importance of numerical and categorical variables against a binary target.
As you roll out more IoT devices, it’s time to add IoT threat modeling – a structured approach to identifying, quantifying, and addressing IoT security risks to your cybersecurity strategy. Moving to IoT threat modeling should be a cross-functional team exercise that you make part of your overall IoT development and management processes and frameworks. If your enterprise isn’t there yet, IoT threat modeling is the first step in growing your IoT security and integrating it into your overall cybersecurity strategy.
Adopting blockchain technology will become a growing priority for CTOs, and equivalent positions, in end user organisations, ranging from property to retail. Adopting blockchain, as with any new technology, will be a challenge. There are numerous suitability and adoption issues. One commonly universal benefit that blockchain technology can bring is the ability to incorporate an efficient financial inclusion mechanism into any business or utility model. Here are some of best practices towards achieving wider adoption.