We present a methodology to grant and follow-up credits for micro-entrepreneurs. This segment of grantees is very relevant for many economies, especially in developing countries, but shows a behavior different to that of classical consumers where established credit scoring systems exist. Parts of our methodology follow a proven procedure we have applied successfully in several credit scoring... [Show full abstract]
Step onto one of IBM's security watch floors and the first thing you'll notice is the screens. Banks and banks of screens, with as many as 250 analysts hawkishly watching over them waiting for one indicator or another to tip into the red. "The amount of information that's flowing into one of these watch floors is very high," says Caleb Barlow, vice president at IBM Security. These watch floors, dotted around the globe, are the heart of IBM's security operation. From here, analysts monitor the network activity of the companies that IBM looks after the security of, searching for signs that they might be under attack.
Cognitive applications have become constant companions at our places of work. We expect smart systems to reduce repetitive workloads and support us in uncovering new Knowledge. As a result, data scientists and software engineers are applying various machine learning algorithms to finetune results and increase processing capabilities. At the same time, critics are ever more loudly calling for more transparency about how these cognitive applications actually function. Companies are also advised to not to manage their AI-driven application environment solely on technical grounds.
In this special guest feature, Ran Sarig, Co-founder and CEO of Datorama, discusses the importance of applying machine learning to data integration or'cleansing' processes with speed and at a scale in order to keep up with the ever increasing number of data sources. And why Big Data needn't be a big mess anymore. Ran has 14 years of management, product, engineering and leadership experience. He co-founded Datorama in 2012 and is its chief executive officer. Prior to this, he worked for MediaMind as its VP of R&D where he managed a group of 130 engineers and product managers.
Even though predictive analytics has been around for quite some time, interest around this topic has increased over the last couple of years. It is no longer enough for a company to accurately record what has happened. Today, an organization's success depends on its ability to reliably predict what will happen – be it predictions about what a customer is likely to buy next, an asset that could require maintenance, or the best action to take next in a business process. Predictive analytics uses (big) data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data, enabling both optimization and innovation. Existing processes can be improved – for example by forecasting sales and spikes in demand and enabling the required adjustments to the production planning.
Discussions around AI cyber defence have traditionally focused on the ability of advanced machine learning to detect the earliest signs of an unfolding attack, including sophisticated, never-seen-before threats. This real-time threat detection overcomes the shortcomings of legacy tools and cuts through the noise in live, complex networks to accurately identify threatening anomalies, including'unknown unknowns'. But while the capability to identify the entire spectrum of threats in their nascent stages before a problem becomes a crisis is incredibly powerful in its own right, it also serves as a fundamental enabler for autonomous response measures, which truly deliver on the promise of artificial intelligence in cyber defense. Before the advent of AI cyber defense, the principal obstacle to achieving autonomous response was determining the exact action that is needed to stop an infection from spreading, while keeping the business operational. By their very nature and definition, traditional approaches to cyber security cannot make the jump from detection to response.
The CBI (Confederation of British Industry) is asking the government to launch an AI commission in 2018 to examine the effect of artificial intelligence on jobs. The CBI, an organisation that speaks on behalf of 190,000 businesses across the UK, has released a report titled'Disrupting the Future' which highlights how firms and the government must pave the way for the adoption of new technologies. It has called on the government to establish a joint commission in early 2018 involving business, employee representatives, academics and a minister to examine the impact of AI on people and jobs. It also hopes the commission will be able to set out an action plan to outline how to raise productivity, spread prosperity and open up new paths to economic growth. Josh Hardie, CBI deputy director-general, said: "The UK must lead the way in adopting these technologies but we must also prepare for their impacts.
Artificial intelligence (AI) is set to substantially disrupt the financial services industry, transforming how we bank, invest, and get insured. AI refers to machines that are capable of performing specific tasks that normally require human intelligence such as visual perception, speech recognition, decision-making, and language translation. AI and related technologies are made possible by the colossal volumes of data we are able to collect and process. AI has been all the buzz these past few years, and according to CB Insights, AI startups raised over US$2 billion in 2016 alone. In the area of financial services, AI is expected to bring major shifts in financial institutions' workforces.
Cisco has revealed plans to acquire San Jose startup Perspica to bolster the firm's previous purchase of AppDynamics in the data analytics arena. On Thursday, Cisco said in a blog post that Perspica is "the first acquisition to support and accelerate the AppDynamics vision." Financial details were not disclosed. The network equipment maker snapped up AppDynamics in January this year in a deal valued at $3.7 billion. AppDynamics is the developer of an enterprise platform suitable for monitoring application performance and business metrics.