A recent survey has found that a majority of cybersecurity professionals believe that artificial intelligence will be used to power cyberattacks in the coming year. Cybersecurity firm Cylance conducted the survey at this year's Black Hat USA conference and found that 62 percent of respondents believe that "there is high possibility that AI could be used by hackers for offensive purposes." "Hackers have been using artificial intelligence as a weapon for quite some time," said Brian Wallace, Cylance Lead Security Data Scientist, to Gizmodo. For the time being, however, cyber security professionals have observed hackers sticking to tried-and-true methods.
Home automation and bots in the workplace are slower to achieve acceptance as well, with only 5.5 percent and 1.0 percent of respondents respectively reporting regular use of these advances in their day-to-day lives. Similarly, only 38.0 percent of West Coast respondents report lack of trust. Only nine percent of respondents trust AI with their financials, and only four percent trust AI in the HR hiring process. Consumers also have opinions about the companies they trust to lead the AI transformation and to deliver AI technology that reliably works.
The gap between ambition and execution is large at most companies. But only about one in five companies has incorporated AI in some offerings or processes. Only one in 20 companies has extensively incorporated AI in offerings or processes. Our research reveals large gaps between today's leaders -- companies that already understand and have adopted AI -- and laggards.
Americans aged 30-44 are most eager to rely on artificial intelligence but they'd rather it was used to do their cleaning over flying a plane. According to a poll conducted by Morning Consult, more than half of people (57 per cent) already realise that they come across AI in their daily lives. When it comes to more complex tasks, people are less trusting, with three out of five people uncomfortable with AI making financial investments. Seven out of 10 people would feel uncomfortable with AI flying an aeroplane of performing surgery.
Yet, the scale of events occurring is huge: many millions of network events per hour, per network element. With standard statistical results, for questions like those in the customer records example, the standard error of a sample of size s is proportional to 1/ s. Roughly speaking, this means that in estimating a proportion from the sample, the error would be expected to look like 1/ s. Therefore, looking at the voting intention of a subset of 1,000 voters produces an opinion poll whose error is approximately 3%--providing high confidence (but not certainty) that the true answer is within 3% of the result on the sample, assuming the sample was drawn randomly and the participants responded honestly. A common trick is to attach a random number to each record, then sort the data based on this random tag and take the first s records in the sorted order. One limitation is that the attribute of interest must be specified in advance of setting up the sketch, while a sample allows you to evaluate a query for any recorded attribute of the sampled items.
While the internet has the potential to give people ready access to relevant and factual information, social media sites like Facebook and Twitter have made filtering and assessing online content increasingly difficult due to its rapid flow and enormous volume. To explore how social media users perceive the trustworthiness and usefulness of these services, we applied a research approach designed to take advantage of unstructured social media conversations (see Figure 3). While investigations of trust and usefulness often rely on structured data from questionnaire-based surveys, social media conversations represent a highly relevant data source for our purpose, as they arguably reflect the raw, authentic perceptions of social media users. To create a sufficient dataset for analysis, we removed all duplicates, including a small number of non-relevant posts lacking personal opinions about fact checkers.
Gartner identified three technology trends it predicts will dominate the enterprise space in the coming years. The trends, or "megatrends," outlined in the recently released Hype Cycle for Emerging Technologies, 2017 (fee charged), will gain prominence and develop over the coming years. In this latest cycle, the company sees digital technologies entering the "Peak of Inflated Expectations Phase," and predicts the use of these technologies in the enterprises is set to explode. Organizations offering IoT/connected technologies reported a 50 percent increase in customer satisfaction, while 44 percent of organizations offering IoT/connected technologies reported satisfaction remaining the same.
This course takes the viewer through the key steps of entering and processing questionnaire/survey data and Likert scales in SPSS, including creating variables in SPSS, entering value labels, using statistical analyses to identify data entry errors, recoding Likert items, computing total (composite) scores, conducting reliability analyses of Likert scales, and computing other statistics, including frequencies, descriptive statistics (mean and standard deviation), and correlations. In addition to this, a number of additional database management skills in SPSS are also covered. Created by an award-winning university instructor with a focus on simple and accurate (step by step) explanations of the material. This course is perfect for professionals looking to increase the data processing skills in SPSS, for those working on survey research, and for students working on theses or dissertations (or other research projects).
The results are the top 3 emerging technology trends that are expected to disrupt business significantly in the next three years. This is the leading trend, with 20 percent of respondents identifying it as driving business transformation. The highest driver of the use of IoT in business transformation is expected to be improved business efficiencies and productivity, followed by faster innovation cycles. Once again, the main driver for the adoption of robots for business transformation is improved efficiency and productivity.
One advance that is beginning to make a positive mark on the incidence of survey fatigue is conversational surveys powered by artificial intelligence. As the customer provides comments or text-based feedback to a question, a conversational survey will facilitate a two-way responsive interaction. To enable the process, conversational surveys use a combination of artificial intelligence, natural language processing and machine learning, and combine this with a brand's own vocabulary to create natural, real-time communication. As the decision tree of responses that chatbots can manage increases, conversational surveys can become more complex and dive into the root-cause of friction across the customer journey.