SAP today introduced the latest advances to SAP S/4HANA Cloud and shared its road map for the industry's next-generation, leading-edge cloud ERP suite. With a new architecture of in-memory technology in combination with contextual analytics, digital assistant capabilities, machine learning and the award-winning SAP Fiori user experience, SAP S/4HANA Cloud enables customers to instantly adjust and adopt business processes and models and act on real-time insight and advice. The announcement was made at SAP Capital Markets Day at the New York Stock Exchange, where SAP executives showcased a combination of strategy and innovation. The ERP offering from SAP provides enterprise-ready functionality for digital business in industry and line-of-business functions, with faster deployment, time to value and lower entry costs of cloud delivery. Industry research firm IDC predicts that the software-as-a-service (SaaS) business applications market will grow 17 percent annually to $103.9B in 2020 from $47.4B in 2015.*

SAP is about as traditional a legacy vendor as you are likely to find, delivering complex on-prem ERP solutions for the largest organizations on the planet. But like everyone else, SAP sees a future in which companies rely less on software installed in private data centers and more on public cloud products to handle the heavy lifting for them. And SAP S/4HANA, the company's public cloud product is designed to address that. While the product was released at the end of 2015, the company is announcing some key updates to the cloud product this week that include some artificial intelligence and machine learning underpinnings, as well as an integrated analytics package to take advantage of the increased intelligence. The idea is to provide more automated insight into the company data being collected by the ERP system.

The field of Artificial Intelligence (AI) was first introduced at a Dartmouth college conference in 1956. At the time, AI founder, Herbert Simon, predicted, "machines will be capable, within twenty years, of doing any work a man can do." But what Simon and the other optimistic early AI experts failed to appreciate are the numerous challenges involved in relying solely on machines - entities that lack the emotion that informs the human decision making process. Despite the fact that the AI field was born more than half a century ago, we've only recently in the last few years made monumental strides in advancing the artificial intelligence game. More specifically, we've started to transition from a model whereby AI assists humans to one whereby humans assist AI.

While Hollywood envisions Artificial Intelligence as one day gaining self-consciousness and starting to wipe out humanity, we're currently still struggling with teaching it how to take a joke. Today's machine learning algorithms are all around us. Machine learning is considered to play a vital role in development of artificial intelligence, but it currently still requires human intervention and tweaking. The merging of man and algorithms allows us to make sense of the terabytes of seemingly unrelated data that's constantly pouring into the internet on a daily basis. Machine learning in the security industry has also proven to be very effective at finding new or unknown malware, based on the features the new malware shares with previously known threats. However, you need to train machine learning algorithms with a dataset that's comprised of known malware samples.

Investing in AI is not an easy job: AI technologies are black boxes and unless you are able to dig into lines of code they may be inscrutable. Simply looking at proof of concepts might not be enough to really understand the underlying stack behind specific applications, and this represents a big barrier for investors to efficiently allocate their capitals. Generalist investors found then alternative ways to discern investable companies from the pile of tech-driven companies out there. Therefore, if a team is composed of scientists/researchers and has patents (obtained or pending), it would already be a good candidate for an investment even without any revenues. This is driven by top tech companies acquiring smaller startups simply for their'brain power' rather than their actual numbers.

Lauding the transformative powers of artificial intelligence (AI) has almost become a cliche, and with good reason. And AI has the potential to transform the way governments design and deliver public services. Our report, published on 6 February, predicts that almost 250,000 public sector workers could lose their jobs to robots over the next 15 years. Governments around the world have recognised the potential of AI, but in practice actual application varies widely. Japan and Singapore are at the forefront of marrying intention and action to harness the power of AI.

Traffic is not just a nuisance for drivers: It's also a public health hazard and bad news for the economy. Transportation studies put the annual cost of congestion at $160 billion, which includes 7 billion hours of time lost to sitting in traffic and an extra 3 billion gallons of fuel burned. One way to improve traffic is through ride-sharing -- and a new MIT study suggests that using carpooling options from companies like Uber and Lyft could reduce the number of vehicles on the road by a factor of three without significantly impacting travel time. Led by Professor Daniela Rus, director of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), researchers developed an algorithm that found 3,000 four-passenger cars could serve 98 percent of taxi demand in New York City, with an average wait-time of only 2.7 minutes. "Instead of transporting people one at a time, drivers could transport two to four people at once, resulting in fewer trips, in less time, to make the same amount of money," says Rus. "A system like this could allow drivers to work shorter shifts, while also creating less traffic, cleaner air, and shorter, less stressful commutes."

As artificial intelligence (AI) becomes more widespread--and as larger numbers of businesses move to put it to use--companies are grappling with the challenge of what exactly they should be doing to prepare. Not surprisingly, for many businesses, a key step is investment. A November 2016 survey by Infosys found that 60% of business and IT decision-makers worldwide were making investments in IT infrastructure. But other areas of focus also emerged in the survey. Nearly half of respondents were tapping external experts for help, and nearly as many cited the need to build AI into their company's "ethos."

Algorithms are instructions for solving a problem or completing a task. Recipes are algorithms, as are math equations. The internet runs on algorithms and all online searching is accomplished through them. Email knows where to go thanks to algorithms. Smartphone apps are nothing but algorithms. Computer and video games are algorithmic storytelling. Online dating and book-recommendation and travel websites would not function without algorithms. GPS mapping systems get people from point A to point B via algorithms. Artificial intelligence (AI) is naught but algorithms. The material people see on social media is brought to them by algorithms. In fact, everything people see and do on the web is a product of algorithms. Every time someone sorts a column in a spreadsheet, algorithms are at play, and most financial transactions today are accomplished by algorithms. Algorithms help gadgets respond to voice commands, recognize faces, sort photos and build and drive cars. Hacking, cyberattacks and cryptographic code-breaking exploit algorithms.

Like most organizations, you want to embed analytics insights in your operational processes and promote a culture of analytical decision making. You want to use machine learning, deep learning, and related technologies to automate decision making when and where it makes sense. These goals might seem both realistic and attainable. After all, software and cloud vendors are pitching you easy-to-use, quasi-automated, self-service tools and consultants promise to help you bridge the gap between the skills you have and the skills they say you'll need. Far from it, says Mark Madsen, a research analyst with information management consultancy Third Nature.