Switching to a new language is always a big step, especially when only one of your team members has prior experience with that language. Early this year, we switched Stream's primary programming language from Python to Go. This post will explain some of the reasons why we decided to leave Python behind and make the switch to Go. The performance is similar to that of Java or C . For our use case, Go is typically 30 times faster than Python.

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. That's why we urge the Government to set up a joint commission on Artificial Intelligence in 2018, involving both business and employee representatives, to better understand the impact on people's lives, jobs and our future economic growth." The group said that only a third of businesses say their company has the skills and capabilities needed to adopt AI technologies and that blockchain can be used across sectors and will require regulator co-ordination to set clear standards. Furthermore, the main concern for businesses looking to use the IoT is the security and privacy of the devices involved.

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.

Today, BBC's R&D team announced a five-year initiative to use machine learning to work out what audiences want to watch. To accomplish this, the team is partnering up with data scientists and experts from UK universities as well as media and tech companies based in Europe and internationally. The Data Science Research partnership intends to create "a more personal BBC" that can entertain in new ways. Researchers will analyze user data and apply algorithms to get marketing and media insights about audiences' preferences. The details are vague for now, but the team says it plans to use machine learning on its own digital and traditional broadcasting content to gain new insights.

Britain's biggest employers are calling for a commission to examine the impact of artificial intelligence on jobs. Amid predictions of a workplace revolution threatening one in five jobs across the UK, the CBI is urging Theresa May to launch the commission from early 2018. It said companies and trade unions should be involved and the commission should help to set out ways to increase productivity and economic growth as well looking into the impact of AI. The business lobby group said almost half of firms were planning to devote resources to AI, while one in five had already invested in the technology in the past year. Companies are increasingly using computers to scour vast datasets in order to spot inefficiencies, while they are also employing machines to control the flow of activity in warehouses and factories and to take meter readings.

The programme has been created based on the experience of leading American and European universities, such as Stanford University (U.S.) and EPFL (Switzerland). Also taken into consideration when creating the faculty was the School of Data Analysis, which is one of the strongest postgraduate schools in the field of computer science in Russia. In the faculty, learning is based on practice and projects. National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more.

Infosys, a global leader in technology services and consulting, is aiming to reinvent the way people consume sport using extensive player data. The Indian firm, which had revenues of $9.5 billion in its last financial year, demonstrated its'Infosys Information Platform (IIP)' during the recent ATP Tennis tournament in London, of which it was a headline sponsor. Speaking to Access AI, the firm's head of energy and services for Europe Mohamed Anis, who joined in 2000, said Infosys uses machine learning to analyse historical data on player performance, which in turn is able to predict behaviour, shot selection, and even a probabilistic outcome of the match itself. Anis (pictured) said the data is delivered in real time and can be used to help spectators view the game/match on an entirely different level – comparable to that of the coach. "Tennis has been around for a very long time," explained Anis.

Next time you're driving down the road or walking down the street, pause to consider how you read your surroundings. How you pay extra attention to the kid kicking a soccer ball around her front lawn and the slightly wobbly, nervous looking cyclist. You make these calls by drawing on a lifetime of social and cultural experience so ingrained you hardly need to think about it. But imagine you're an autonomous car trying to do the same thing, without that accumulated knowledge or the shared humanity that lets you read others' nuanced behavioral cues. Treating every pedestrian, cyclist, and vehicle as an obstacle to be avoided might keep you from hitting anything, but it could just as easily keep you from getting anywhere.

The number jobs in artificial intelligence (AI) in the UK has risen dramatically in the last three years, according to Indeed. Since 2014, the number of available AI roles in Britain has increased by 485% - representing a significant spike in demand for employees with the appropriate skills for the job. Yet Indeed's data also reveals there are over two times as many AI jobs available than there are suitable applicants, with a ratio of 2.3 roles available per candidate searching in the last quarter. Interest in AI roles has risen more steadily by 178% in the past three and a half years, not quite high enough to meet the fivefold surge in postings. The popularity of software in innovations including smart home devices and customer service chat bots demonstrate how the industry is developing at pace.

We introduce the concept of local bucket error for the mini-bucket heuristics and show how it can be used to improve the power of AND/OR search for combinatorial optimization tasks in graphical models (e.g. MAP/MPE or weighted CSPs). The local bucket error illuminates how the heuristic errors are distributed in the search space, guided by the mini-bucket heuristic. We present and analyze methods for compiling the local bucket-errors (exactly and approximately) and show that they can be used to yield an effective tool for balancing look-ahead overhead during search. This can be especially instrumental when memory is restricted, accommodating the generation of only weak compiled heuristics. We illustrate the impact of the proposed schemes in an extensive empirical evaluation for both finding exact solutions and anytime suboptimal solutions.