it software


Can graphical passwords keep us secure online?

ZDNet

Passwords are designed to NOT play to human strengths. In the days of ASCII terminals and command line interfaces, passwords made some kind of sense. But today, the primary online interface is a graphically rich mobile device. So why are we still stuck with alphanumeric passwords?


Unlocking The Power Of Artificial Intelligence Should Be A Priority For Infrastructure Leaders

Forbes Technology

As the infrastructure world becomes saturated with progressively sophisticated digital technologies, public and private sector infrastructure leaders will be forced to adopt a new base of knowledge and new set of skills. Many of these decision makers are accomplished engineers, but with mechanical, civil, structural or electrical backgrounds. Their expertise and experience remains valuable and relevant, but must today be augmented by perspectives from computer science and software engineering in order to meet the demands and expectations of today's citizen-consumers. These changes also mean officials will have to source new partners and vendors -- ones like Xaqt, Rapid Flow Technologies and Pluto AI that can supplement traditional infrastructural intelligence with digital intelligence.


Machine learning in finance: The Why, what & how

#artificialintelligence

Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. Let's see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology. We can define machine learning (ML) as a subset of data science that uses statistical models to draw insights and make predictions. The chart below explains how AI, data science, and machine learning are related.


What is Cognitive Robotic Process Automation (CRPA)? by Venkateshwarlu Kakkireni

#artificialintelligence

Cognitive Robotic Process Automation is the next step in the evolution of robotic process automation trends. Many of the leading robotic process automation companies are already eyeing the big shift towards the cognitive automation. Cognitive robotic process automation is basically a combination of robotic process automation and Data Analytics, which together make it easy and effective to manage processes that are information-intensive, in an intelligent and efficient manner. By that definition, it is a marriage between artificial intelligence and cognitive computing methods. By incorporating artificial intelligence, cognitive automation broadens the scope and depth of actions that would typically be associated with RPA.


Best Machine Learning Tools: Experts' Top Picks

#artificialintelligence

The best trained soldiers can't fulfill their mission empty-handed. Data scientists have their own weapons -- machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts. And that's why we interviewed data science practitioners -- gurus, really --regarding the useful tools they choose for theirprojects. The specialists we contacted have various fields of expertise and are working in such companies as Facebook and Samsung. Some of them represent AI startups (Objection Co, NEAR.AI, and Respeecher); some teach at universities (Kharkiv National University of Radioelectronics).


How Data Scientists Can Tame Jupyter Notebooks for Use in Production Systems

#artificialintelligence

Uncounted pixels have been spilled about how great Jupyter Notebooks are (shameless plug: I've spilled some of those pixels myself). Jupyter Notebooks allow data scientists to quickly iterate as we explore data sets, try different models, visualize trends, and perform many other tasks. We can execute code out-of-order, preserving context as we tweak our programs. We can even convert our notebooks into documents or slides to present to our stakeholders. Jupyter Notebooks help us work through a project from its earliest stages to a point where we can say a great deal.


Machine learning in finance: Why, what & how

#artificialintelligence

Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. Let's see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology. We can define machine learning (ML) as a subset of data science that uses statistical models to draw insights and make predictions. The chart below explains how AI, data science, and machine learning are related.


Practical Apache Spark in 10 minutes. Part 1 - Ubuntu installation

#artificialintelligence

Apache Spark is a powerful open-source processing engine built around speed, ease of use, and sophisticated analytics. It has originally been developed at UC Berkeley in 2009, while Databricks was founded later by the creators of Spark in 2013. The Spark engine runs in a variety of environments, from cloud services to Hadoop or Mesos clusters. It is used to perform ETL, interactive queries (SQL), advanced analytics (e.g., machine learning) and streaming over large datasets in a wide range of data stores (e.g., HDFS, Cassandra, HBase, S3). Spark supports a variety of popular development languages including Java, Python, and Scala.


What machine learning means for software development

#artificialintelligence

Check out the "Executive Briefing: Have we reached peak human? Hurry--early price ends July 20. Machine learning is poised to change the nature of software development in fundamental ways, perhaps for the first time since the invention of FORTRAN and LISP. It presents the first real challenge to our decades-old paradigms for programming. What will these changes mean for the millions of people who are now practicing software development? Will we see job losses and layoffs, or will see programming evolve into something different--perhaps even something more focused on satisfying users?


AI Weekly: How to regulate facial recognition to preserve freedom

#artificialintelligence

Today Microsoft president Brad Smith called for federal regulation of facial recognition software. "In a democratic republic, there is no substitute for decision making by our elected representatives regarding the issues that require the balancing of public safety with the essence of our democratic freedoms. Facial recognition will require the public and private sectors alike to step up -- and to act," Smith wrote in a blog post. Recent events explain why Smith is speaking out now. Last month, while the majority of U.S. citizens was outraged about the idea of separating families who unlawfully entered the United States, Microsoft was criticized by the public and hundreds of its own employees for its contract with Immigration and Customs Enforcement (ICE).