Driverless car safety may be taking a further leap forward with new laser-based system that could allow vehicles to ‘see’ obstacles before they come into view.
A herd of cows or a broken-down car? Unexpected obstacles can prove a big risk to drivers -but cars of the future might be able to anticipate the danger. A team of researchers have come up with a new laser-based system which produces images of objects that are hidden around a corner – a development they say could allow driverless vehicles to see obstacles before they come into view – obviously having massive potential in terms of safety.

Current technology involves sending laser pulses towards a surface and measuring the time it takes for light to be reflected – then the data is used to build a three-dimensional model of the surface. The new technology takes the idea further by using lasers to see around corners: a laser and photon detector are placed in front of a wall next to an object and are separated from the object by a partition. Laser pulses are then fired at the wall at an angle but instead of collecting the light that bounces directly back off the wall toward the detector; the light that bounces off the wall, hits the object and is then scattered by it is collected as the second, third and fourth bounces encode the hidden objects.

Previous approaches to the problem have involved directing laser pulses at one point on the wall and then collecting signals from another point, whereas this technique points both the laser and the detector at the same point on the wall.

By using the timing of the signals to remove the signals from light that bounced back directly, those remaining are rapidly processed using an algorithm to reconstruct the hidden object – a simple tweak with huge potential as it uses less memory and processing to generate a higher resolution image.

After first developing an algorithm by creating computer models of how laser pulses would bounce off a model of a rabbit hidden by the partition, the team applied the system in real life, including capturing an image of an Exit sign.

It seems that the highly reflective nature of road signs and bicycle reflectors make the technology a good fit for driverless cars, as the research uses sensors similar to those already used in autonomous vehicles.

Inevitably, there are still obstacles to overcome. The initial scanning of the wall can take anywhere from a minute to a couple of hours and the system needs to be better at detecting objects that are not highly reflective or stationary, such as children or wild animals. It also needs to be used outdoors in bright, sunny conditions.

So, it looks like we’re on the way to having automated vehicles which can predict or detect what is happening ahead – not only what is going on in their own lane but also in the surroundings like pedestrian paths or on the other side of the corner.

It may seem like science fiction- but cars seeing round corners are well and truly on the way.

As companies invest more and more in big data, an employee possessing ‘Data Skills’ is going to be someone to watch. Many employment opportunities are directly involved with big data: big data developer, Hadoop developer, big data engineer, data analytics engineer. Professionals with big data expertise are becoming more and more in demand, so brushing up on your data skills might be worthwhile when building your CV.

Machines can now have access to vast pools of data, and machine algorithms are replacing human decision making, and big data is predicted to become the base of businesses in the future. A career in the data domain is, on average, 40% higher paid than other IT skills, and by the end of 2018 there will be a shortage of 190,000 big data professionals in the US alone.
There has been an unbelievable rise in demand for big data skills, with an increase of 3977% since 2011. 2018 is clearly the best time to heighten your changes with the most sought-after big data skills. These are the 5 must-have big data skills needed to land any jobs I big data in 2018:

1) Data Visualisation
In the last 5 years, demand for data visualisation skills has grown by 2574% in the last 5 years. Data Visualisation sets the business context for what the data tells you, makes it easier for non-analysts and stakeholders to understand the data, and make decisions based on it. Employees with Data Visualisation skills tell the business about the shape of data, and what insights the data reveals through tools such as Tableau and Qlikview, and the demand for these skills is increasing consistently.

2) Apache Hadoop
This technology is equivalent to big data, and is a decade old, but the demand for professionals with experience with Hadoop is not going to decrease any time soon. Hadoop is a powerful big data platform that can cause real problems if not handled skilfully, and a knowledge of how to tweak the core components within the Hadoop technology is necessary for employees looking for work in this area. What’s more, Hadoop has been huge throughout 2017 and is going to get even more popular in 2018, and demand for the skills required to successfully implement Hadoop environment will only continue to be in demand in 2018.

3) Apache Spark
Within the big data industry, Apache spark has gained broad familiarity and popularity owing to the speed of its analysis and processing. This is one of the fastest growing big data skills for job seekers with job postings for employees with spark skillset increasing 120% year-over-year. The major barrier to its adoption into businesses is the issues integrating it without employees with the skills required. Demand for expertise in this area, therefore, will only rise.

4) NoSQL Database
If Apache Hadoop skills are one side of big Data analysis, NoSQL database skills are the other. 90% of the data generated over this decade is likely to be unstructured, and managing this unstructured big data requires professionals with skills in NoSQL database. Professionals with expertise in this area are in such high demand, with the average salary for a Senior Software Engineer with NoSQL skill topping £83,000 a year.

5) Data Science and Analytics
Data science technologies are hugely impacting the economy, with the technology and finance sectors quickly embracing these skills. The demand for data science and analytic skills is predicted to grow by 15% in the next 5 years. Data scientists and advanced analysts are the fastest growing demand for job roles and are anticipated to see demand increase by 28% in 2018.
The demand for these skills is not only rising, but here now, as employers struggle to find candidates to meet their big data needs. Whether you are beginning your career, or looking for better opportunities, it’s important you are aware of the in-demand skills in big data work. Once you master the skills listed in this post, opportunities in this industry are everywhere, and they are the highest paying technology career opportunities. If you’re considering this area as a career path, 2018 is the year to do it.

After years of waiting, Artificial Intelligence is finally making real steps forward, particularly within Fortune 1000 corporations. A survey taken in 2018 represents nearly 60 different industry leading companies, many of which were Fortune 1000 businesses and bellwether, and 93% of respondents were C-level executive decision-makers.

This research reveals that 97.2% of companies are investing in, building or launching big data and AI initiatives. Also, the greater availability of data within organisations is empowering AI initiatives, and so big data and AI are becoming increasingly intertwined. A direct correlation between big data accessibility and advancements in AI advancements can now be seen, and large corporations have access to volumes and sources of data that can be used to feed AI algorithms.

Increasing access to data means that AI algorithms can detect patterns and work to understand behaviour and produce a range of benefits including real-time consumer credit approval, and new product offers.

Financial service companies have long been driving the industry, as their large volumes of transactional and customer data they gather has fuelled the development of data management and data governance processes over the last decades. These analytics are used to assess customer profitability, identify target markets and manage risk. For industries such as life sciences, despite being newer to data management than other industries, have vast caverns of scientific and patient data that have remained all but undisturbed, despite their potential for insight.

Data-driven competitors such as Google, Facebook, Amazon and Apple have cultivated highly agile databases of information, are pushing companies to invest more in big data and AI initiatives in 2018 to keep up. The 2018 survey revealed that 71.8% of executives state that investments in AI will have a huge impact on their ability to stave off disruption throughout the next decade. As the fear of disruption indicates, the number of organisations investing in AI and big data will continue to increase.

Now, 73% of executives surveyed indicate that investments in AI and big data are beginning to yield real results. The advanced analytics successfully improve decision-making, accelerate time-to-market for new products and services, and to improve customer service. Over 1/4th of executives report success in monetising their investments, which is the goal for the majority of organisations.
As companies look to the future, there is the growing belief that AI holds the key to improvement, and 93% of executives identify that their company is investing in AI for the future. Without investment in this technology, competing will become difficult, and those with developed AI capabilities will be best able to compete with agile, data-driven competitors of the future.

Analysing data has become necessary for all departments across the corporate landscape, and as a result, the demand for technical solutions has increased. So, the call for the democratisation of data is also getting louder. Data democratisation is the idea that every individual has access to data, without obstacles or hidden costs, it forms part of the new GDPR regulations and is essential if companies are going to fulfil data access requests.

Achieving this goal, however, is harder than it might appear.

The first issue is that the collection and analysis of data, despite being accessible, can be very time consuming. Because of this, companies tend to outsource the process to other vendors, and the costs involved can be high, especially as these vendors are competing with even bigger corporations like IBM. This is a problem because the essence of data democratisation is freedom, and these expensive solutions do not encourage that.

Once the data has been collected, the next step in the process is to make sure that it is as easy as possible to get access to it. You can have all the data in the world, but until it is accessible, it hasn’t been democratised. The lack of speed in terms of getting information and insight from the raw data can be a major stumbling point when it comes to full democratisation.

These problems have led to many companies adopting a more do-it-yourself approach. By using step by step processes, rather than the all-encompassing service offered by traditional vendors, money is saved. However, the issue here is that to do that, significant levels of skill are required, which immediately excludes people without them from benefiting from the data. Thus, it isn’t fully democratised. One of the potential ways to solve this is to use intelligent AI. Provided they remain affordable, then DIY services, incorporating this technology, could be the way to achieve the goal of total data democratisation.

Adopting drones for commercial purposes is becoming more and more popular, but it involves a transition, moving from valuing the technology itself, to the data it gathers. Too often the assumption when someone talks about large drones is that it is a military asset. But many different sectors can benefit from the data acquisition capabilities of a drone ecosystem, even if they aren’t a complete solution to all issues.

Depending on what the specific requirements of the clients are, a system of drones can be used for general surveillance to gather information about a wide area, or, can be used in a more targeted way to obtain more specific pieces of data. This adaptability allows for more customisable data solutions.

What is interesting about drones is that they offer the same service you could expect from other technologies, but they go about it in a revolutionary way. For example, you can inspect an aircraft using people and cranes, but it is a time-consuming process. The drones will still inspect the aircraft, but it can be done much quicker. The efficiency of this technology, and the potential to speed up existing processes, is exciting.

Whilst the technology is complicated, it can be simplified quite dramatically to meet the needs of the clients. Regulations differ from country to country, and it is marginally easier to take to the skies in the USA as opposed to Europe, but drones are becoming more commonplace all over the world.

The first transatlantic data transfer happened over 150 years ago, and since then, it has become the most valuable resource in the world. More money is involved in the multinational transfer of data that the cross border delivery of physical goods.

And as data becomes more important, the acquisition, control and manipulation of it are vital in determining the success or failure of businesses. However, because it is so vital, large companies have been frequently accused of being careless with the mountains of data at their disposal, as well as lobbying for looser and looser restrictions on the regulations surrounding data flow. This may be a concern for some, especially at a time where concerns about privacy are very high.

Data acquisition in itself is not sufficient, what is done with that data is crucial.

Companies in America have been switched on to this trend for a while, but for those outside the US, it has taken some time to get onboard. And the reason it is so important is because so much data is no longer just personal. Financial, industrial and commercial data is vital in terms of many profit making activities. The concern is that, as we see in China, the regulation of data can be used as a tool to aid a ruling regime, and thus people are understandably worried about how theirs is used, and who has control.

Thanks to a new piece of European law, EU citizens will, in 2018, have more control than ever when it comes to how their data is stored and used. The new legislation, known as the General Data Protection Act, will be the biggest legal overhaul in the data gathering sphere for 20 years. The new rules allow customers to more easily remove their data from databases, as well as provide their data to competitor services with greater ease, and require companies to provide a notification to customers within three days if there is a hack.

Any country that has companies that may wish to trade with the EU must comply or face a fine of 20 million euros, or 4% of total global income, depending on which is more. GDPR affects any company that processes data in Europe. So, if an customer of an American Mobile company visits the UK, and that company monitors, or acquires any data, then they are governed by GDPR.

Given that for many, losing out on billions worth of trade with the largest trading bloc in the world is unthinkable, EU citizens will soon find themselves with more control over their data than ever before and companies having to be more and more careful with what to do with the data they acquire.

A lot of data acquisition deals with technical information, monitoring of systems. Where that data can be directly, or indirectly related to an individual, then it potentially becomes problomatic. Imagine a situation where a device is measuring the brake performance on a test vehicle – where the data is collected and processed, alongside the details of the driver, or tester – then that data could be covered by GDPR, and organisations collecting or processing it could face huge fines if they don’t process it properly.

Now for some countries like Japan, Israel and New Zealand, who already have data privacy standards judged to be on par with Europe, then the transition process that businesses must undertake will be a reasonably smooth one. But for emerging markets, the high cost involved with administering the changes may result in them being left out in the cold.

Europe has been at the forefront of data privacy laws for a while now, but with the vast swathes of data American companies like Amazon and Facebook can collect, EU lawmakers are creating new legislation to increase protection.