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Find Out If Your Job Will Be Automated

The most recent jobs report, showing a high unemployment rate in 2018, also portrays the global economy capable of generating plenty of work. But what if, in the not-too-distant future, there won’t be enough jobs to go around?

That’s what some economists believe will happen as robots and artificial intelligence increasingly becomes capable of performing human tasks. Researchers, for example, estimate that nearly half of all jobs may be at risk in the coming decades, with lower-paid occupations among the most vulnerable.

Wondering how vulnerable your job might be? Refer to the chart below to see what the researchers think is the probability of your job being automated.

Automated system identifies dense tissue, a risk factor for breast cancer, in mammograms

Deep-learning model has been used successfully on patients, may lead to more consistent screening procedures.

Researchers from MIT and Massachusetts General Hospital have developed an automated model that assesses dense breast tissue in mammograms — which is an independent risk factor for breast cancer — as reliably as expert radiologists.

This marks the first time a deep-learning model of its kind has successfully been used in a clinic on real patients, according to the researchers. With broad implementation, the researchers hope the model can help bring greater reliability to breast density assessments across the nation.

It’s estimated that more than 40 percent of U.S. women have dense breast tissue, which alone increases the risk of breast cancer. Moreover, dense tissue can mask cancers on the mammogram, making screening more difficult. As a result, 30 U.S. states mandate that women must be notified if their mammograms indicate they have dense breasts.

But breast density assessments rely on subjective human assessment. Due to many factors, results vary — sometimes dramatically — across radiologists. The MIT and MGH researchers trained a deep-learning model on tens of thousands of high-quality digital mammograms to learn to distinguish different types of breast tissue, from fatty to extremely dense, based on expert assessments. Given a new mammogram, the model can then identify a density measurement that closely aligns with expert opinion.

“Breast density is an independent risk factor that drives how we communicate with women about their cancer risk. Our motivation was to create an accurate and consistent tool, that can be shared and used across health care systems,” says Adam Yala, a PhD student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and second author on a paper describing the model that was published today in Radiology.

The other co-authors are first author Constance Lehman, professor of radiology at Harvard Medical School and the director of breast imaging at the MGH; and senior author Regina Barzilay, the Delta Electronics Professor at CSAIL and the Department of Electrical Engineering and Computer Science at MIT and a member of the Koch Institute for Integrative Cancer Research at MIT.

Mapping density

The model is built on a convolutional neural network (CNN), which is also used for computer vision tasks. The researchers trained and tested their model on a dataset of more than 58,000 randomly selected mammograms from more than 39,000 women screened between 2009 and 2011. For training, they used around 41,000 mammograms and, for testing, about 8,600 mammograms.

Each mammogram in the dataset has a standard Breast Imaging Reporting and Data System (BI-RADS) breast density rating in four categories: fatty, scattered (scattered density), heterogeneous (mostly dense), and dense. In both training and testing mammograms, about 40 percent were assessed as heterogeneous and dense.

During the training process, the model is given random mammograms to analyze. It learns to map the mammogram with expert radiologist density ratings. Dense breasts, for instance, contain glandular and fibrous connective tissue, which appear as compact networks of thick white lines and solid white patches. Fatty tissue networks appear much thinner, with gray area throughout. In testing, the model observes new mammograms and predicts the most likely density category.

Matching assessments

The model was implemented at the breast imaging division at MGH. In a traditional workflow, when a mammogram is taken, it’s sent to a workstation for a radiologist to assess. The researchers’ model is installed in a separate machine that intercepts the scans before it reaches the radiologist, and assigns each mammogram a density rating. When radiologists pull up a scan at their workstations, they’ll see the model’s assigned rating, which they then accept or reject.

“It takes less than a second per image … [and it can be] easily and cheaply scaled throughout hospitals.” Yala says.

On over 10,000 mammograms at MGH from January to May of this year, the model achieved 94 percent agreement among the hospital’s radiologists in a binary test — determining whether breasts were either heterogeneous and dense, or fatty and scattered. Across all four BI-RADS categories, it matched radiologists’ assessments at 90 percent. “MGH is a top breast imaging center with high inter-radiologist agreement, and this high quality dataset enabled us to develop a strong model,” Yala says.

In general testing using the original dataset, the model matched the original human expert interpretations at 77 percent across four BI-RADS categories and, in binary tests, matched the interpretations at 87 percent.

In comparison with traditional prediction models, the researchers used a metric called a kappa score, where 1 indicates that predictions agree every time, and anything lower indicates fewer instances of agreements. Kappa scores for commercially available automatic density-assessment models score a maximum of about 0.6. In the clinical application, the researchers’ model scored 0.85 kappa score and, in testing, scored a 0.67. This means the model makes better predictions than traditional models.

In an additional experiment, the researchers tested the model’s agreement with consensus from five MGH radiologists from 500 random test mammograms. The radiologists assigned breast density to the mammograms without knowledge of the original assessment, or their peers’ or the model’s assessments. In this experiment, the model achieved a kappa score of 0.78 with the radiologist consensus.

Next, the researchers aim to scale the model into other hospitals. “Building on this translational experience, we will explore how to transition machine-learning algorithms developed at MIT into clinic benefiting millions of patients,” Barzilay says.

Without emotional intelligence, artificial intelligence isn’t so smart

Vahé Torossian – Corporate Vice President, Microsoft Corporation & President Microsoft Western Europe

I truly believe that Artificial Intelligence (AI) carries enormous potential to make the world a better place and drive transformational change in some of the most important aspects of our lives – how we live, play, interact with each other and, last but not least, work. AI is also full of surprises. When it comes to AI in the workplace, we’ve noticed that a key and common trait of companies that use AI successfully is actually — Emotional Intelligence (EQ).

Simply defined, EQ is the ability to identify and understand emotions. In a professional context, it means having the ability to handle relationships with empathy, understand what motivates people, and creating an open and collaborative environment (with technology or other tools) that empowers people to do their best work. Throughout my career, I’ve seen first-hand how EQ can take an organization to a whole new level.

We recently learned a lot about AI, and its fascinating relation with EQ, from a new study commissioned by Microsoft and conducted by Ernst & Young. Released publicly today, Artificial Intelligence in Europe seeks to help us understand the AI strategies of 277 major companies across seven business sectors and 15 countries in Europe. It examines how ready these companies are to adopt AI, how the organizations rate the impact and benefits from AI implementations, and what they perceive as the keys to success.

The research found a strong and clear correlation between the maturity of AI deployments by European companies and how those organizations rate themselves on EQ. A solid majority—80 percent—of the companies most advanced in AI considered themselves to be “strongly emotionally intelligent”. On the flip side, only 16 percent of the companies considered least mature in AI rated themselves as more than moderately competent in EQ.

The correlation between EQ and AI is not obvious. Researchers asked companies about eight organizational capabilities considered necessary to successfully harness AI. EQ was rated as the least important. Companies that are less mature in their use of AI are often focused on more immediate needs such as data management and advanced analytics, which organizations ranked as the most important capabilities considered by the study.

This makes a lot of sense to me. At Microsoft we believe the promise of AI lies in what it can do to amplify our ingenuity – the power of human PLUS machine. It may well be that you must have an emotionally intelligent business culture, open to change and to new ways of working, to successfully use AI. Think about it… since AI is relatively new for most organizations, solving business and customer challenges with AI often requires that systems be designed and built from the ground up. Doing that requires business acumen, technological savvy, and a willingness to embrace the unknown. Another interesting finding in the research was that 57 percent of the companies interviewed expect that AI will have a high impact on business areas that are entirely unknown to the company today. Business leaders who are ready to embrace and tackle the unknown are already demonstrating the kind of openness that is fundamental to EQ.

I believe that businesses can open the door for tremendous growth opportunities by fostering a culture that is emotionally intelligent and that empowers workers with AI tools. 61 percent of the 277 companies included in the research expect AI to free up employees to do what people do best: think creatively and figure out how to use technological tools to drive business success, optimize operations, and engage customers in new and exciting ways. It’s hard to imagine a business leader that doesn’t want that sort of outcome. One of our challenges is that, in Europe, we have a long way to go to get there; just four percent of respondents said that AI is contributing to company-wide processes today. So all I see is opportunity!

Technology for technology’s sake has never been the answer. We must be aligned on the ultimate impact technology can have and be working toward clear and common goals – for the benefit of our customers and our society. To impact real change and to harvest the huge potential of AI, leaders must establish that clarity and create an open and collaborative culture that supports people with intelligent technology, so they can bring their very best to work every day. Then we will truly see the power of AI, to create a better tomorrow.

Impossible To Ignore: The Importance Of IT Governance

Effective IT governance is a critical tool for CIOs to align their organizations and efforts to support business strategy and create shareholder value. Given the rapidly changing and evolving technology options that confront CIOs and business leaders, making sure the right decisions are being made about investments in IT is an essential priority.

There are many misconceptions about what constitutes a comprehensive IT governance model and how it is implemented. IT governance is more than just:

  • Having a steering committee that meets periodically to review and approve IT plans and budgets
  • Involving the business on an annual basis to assist in assigning IT priorities
  • Using financial metrics such as ROI to determine whether to invest in specific initiatives
  • Instituting best practices to ensure projects are completed on time and within budget
  • Measuring and reporting on user satisfaction of IT services

While all of the above are important yardsticks to assess the impact of IT, taken one by one they do not guarantee that IT is contributing to the type of business performance that provides a competitive advantage and achieves enterprise business goals. Most of all, they do not constitute an effective IT governance program.

How Best to Think About IT Governance

IT governance comprises a decision framework and set of processes that allow CIOs and management to articulate desired outcomes through programs that enable the organization to attain these results. The decision framework and the corresponding tools and processes to support them must be clearly communicated so that day-to-day activities and decisions are made within this context. In other words, IT governance needs to instil behaviour and awareness that is understood at all levels in the organization, not just by senior management.

Clearly, the desired outcomes that shape IT will vary between industries and organizations. For example, some enterprises may focus on product innovation and accelerated go-to-market strategies while others may strive to create operational efficiencies throughout the value chain. CIOs may also encourage management to consider new technologies such as a big data, real-time analytics initiative or social-media-based customer satisfaction programs to support business performance.

The essential success factor, regardless of the specific initiative undertaken, is the linkage to tangible, measurable top-line or bottom-line business outcomes. As tempting as the latest technology or trend might be, organizations must always calibrate their IT endeavours against this metric to ensure they are not investing financial and human capital where it will yield minimal return and offer no strategic value.

CIOs must take the lead in helping place the organization’s competitive model within the governance-making framework so that the right decisions are being made and, ultimately, institutionalized across the enterprise. From a top-down perspective this means:

  • Linking business strategy to the IT programs that will be undertaken and funded; this will be reflected and communicated within the IT planning process.
  • Aligning IT spend and investments to ensure that they reflect the appropriate strategic initiatives. This is a continual process and not just part of the annual budgeting cycle.
  • Staffing the IT organization with the necessary skills and resources to effectively execute the committed programs.
  • Implementing effective risk management processes that ensure regulatory compliance, accountability, transparency and resiliency.
  • Creating a financial scorecard that tracks approved IT investments to each desired outcome measured in delivered business benefits.

Institutionalizing IT Governance

The missing link between a well-thought-out plan endorsed by management and actualization is often the absence of tactical processes and policies both inside and outside of IT. Some critical and foundational disciplines include:

Business Integration

The emergence of enterprise architectures – solutions that support end-to-end business processes – require CIOs to advocate for far greater business involvement than was traditionally required for “siloed” applications. Prerequisites are (1) business sponsorship at an executive level to provide the sense of urgency and commitment of mindshare and resources required and (2) business process owners who oversee and control the impact of new technology throughout the organization. Without these two ingredients, any strategic project will be viewed as IT-centric with little accountability from the business and, by extension, limited commitment to the desired outcomes.

Program Management

Consistent practices in managing IT projects and delivering solutions within agreed-upon parameters is a basic building block for most organizations. However, within the broader context of an IT governance framework, program management must incorporate metrics that were used within the governance framework. This would include not only the investment analysis but also the desired outcomes that drove the decision-making process. These metrics can be incorporated within dashboards that will help management view progress, benefits and the effectiveness of their decisions. As all effective management practices, IT governance needs to be continually reviewed, assessed and refined with proper measurement and transparency. Program management is essential to bridging decisions to the execution of strategic plans.

Portfolio Management

By their very nature, IT architectures consist of numerous technical layers and components, making them difficult to relate to in terms of business activities and decision making. Portfolios are useful tools for CIOs to integrate views of IT services and solutions to senior management so that they can be associated with desired outcomes. Often this will result in moving toward architecture standardization as an added dividend that will yield long-term benefits. The move to integrated solutions across the enterprise will require a restructuring of the portfolio as an overall strategy that will reduce IT costs and deliver greater operational efficiencies. The portfolio dimension is another key criteria that must be incorporated within management scorecards to guide future technology investments.

How Best to Move Forward

Developing a comprehensive IT governance program can be a daunting task even for organizations with mature management practices. The best place to start is to become familiar with the COBIT 5 framework and principles. ISACA (Information Systems Audit and Control Association) offers many valuable tools and information that will help with education and putting into place a roadmap for the IT governance journey.

Additionally, consider utilizing an experienced practitioner that can help implement practical and proven strategies to formulate an IT governance program and roadmap.  They can also assist in engaging senior management in adopting the necessary practices that will lead to acceptance across the broader organization.

It cannot be stressed enough that IT governance is an ongoing journey that will continually evolve, not a one-time destination.  It is up to CIOs to lead the way by helping their organizations think about, evaluate and adopt the “right” IT strategies for their businesses.

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Robots are reading your résumé, so here are tips to meet their approval

Companies are increasingly using AI to take the guesswork out of job searches and find the candidates whose résumés match what they are looking for. The first step to a successful job hunt is knowing how the algorithms work. Then, tailor your résumé to use AI to your advantage.

Without even thinking about it, we interact with artificial intelligence every day. Siri finds nearby pizza places or dry cleaners. Alexa turns on lights and gives the day’s forecast. So it may come as no surprise that AI is now a deep but unseen part of your job hunting.

Just as spellcheck alerts you to a typo, other algorithms pore over your electronically submitted résumé for misspellings, grammar and information about your work history.

With thousands of previous versions of a job that can be scanned, the algorithm uses the available data on résumés to find the best candidates for a talent recruiter, according to Ian Siegel, CEO of ZipRecruiter, an online jobs site.

“Machine learning can cherry-pick and rapidly learn from the employer how to do a lookalike search,” Siegel said. “That turns out to be by far the best method you can use to match.”

On the other side of the job hunt, AI can match a person to a pool of applicants who have experience or skills in common with the job seeker, and show the jobs they’ve applied to.

“AI is the new version of keyword algorithms,” which have been around since the 1990s, said Robert Meier, a job transition expert and CEO of JobMarketExperts, which deals with a range of employment issues. “Typically, the algorithm looks for continuity of work history, job title progression and education,” he said. Specific companies may have different metrics they look for, such as software experience or credentials.

What has changed is the number of applicants. Digital applications are easy and free, Meier says, and any job opening now has so many more candidates for a company to screen.

But most are eliminated almost immediately, and only the top 2 percent of candidates make it to the interview, Meier said.

The algorithms are the table stakes to get you in the door, Siegel said. Give yourself every advantage of getting yourself into the best-match list.

Cover letters still matter

The algorithms are the table stakes to get you in the door, Siegel said. Give yourself every advantage of getting yourself on the best-match list.

More résumés submitted on apps and tablets mean fewer cover letters.

“But it’s still an opportunity to stand out and give yourself an advantage,” Siegel said.

He recommends every cover letter include what he calls an essential sentence.

“Put things in the simplest, most straightforward language possible.”-Ian Siegel, CEO of ZipRecruiter

Do some research on the company you’re applying to and make sure your letter says, “I am so excited to apply for this job, because …” Fill in that blank, Siegel advised, with a phrase such as “I love your product” or “My skills are a perfect match to take your product to the next level.”

Convey your availability and enthusiasm to project the most attractive version of yourself, Siegel said, and use this as a best practice to approach an opportunity that really interests you.

Given all these behind-the-scenes algorithms, job hunters need to know how their résumé looks to computer “eyes” rather than human ones. Here are five things to do on résumés you submit electronically.

1. Be straightforward

“Put things in the simplest, most straightforward language possible,” Siegel said.

Clearly list your skills and the years of experience you have with each one.

Instead of “professional sound engineer with varied experience in wide variety of software,” check the job description for specifics. Better to say you’re a sound engineer with four years’ experience using Avid Pro Tools. “The algorithms are really good at deducing these are the key skills for a job,” Siegel said.

2. Spelling counts

It’s critical to remember that algorithms on job sites scan for a range of signals.

“You might be cavalier about spelling and grammar,” Siegel said. “That’s an easy signal.”

For most companies, that means your résumé is automatically discarded.

3. Have an up-to-date format

Algorithms try to turn the information on your résumé into usable data, said Siegel, so make sure you use a traditional, text-based format.

Don’t use Photoshop on your résumé: The algorithm can’t derive data from a picture. “Use a modern text editor,” Siegel said. “WordPerfect will make for a challenging document.”

4. The magic of ‘results’

A résumé filled with results — not duties and responsibilities — attracts employers like moths to a flame, JobMarketExperts’ Meier said.

Phrase your accomplishments as revenue, income or money saved. Perhaps you made some aspect of a company function more efficient or found a way to cut costs.

A résumé that includes specific numbers, percentages and quantities will get a closer look.

5. Have a mobile-ready résumé

Most job-seeking activity happens on a cellphone or tablet, but those are not particularly text-friendly.

“Create your résumé and cover letter in the right format on a desktop,” Siegel said. Use a cloud-based service such as Google Drive so you can apply on any site using a mobile device.

Artificial Intelligence (AI) Is Already Changing Hiring Process

While critical to business success, the recruiting process as a whole is both time-consuming and incredibly costly. The average company spends roughly 8-12 percent of a candidate’s salary in recruiting costs, although the number should be closer to 6 percent.

By introducing the right technology, recruiting teams can streamline the process and significantly reduce spending. Artificial intelligence is particularly poised to assist HR teams with the repetitive, time-consuming tasks of recruiting. This could potentially be saving thousands of dollars per employee per year and improving the quality of hires overall.

Given that 35 percent of hiring managers see AI as a top trend impacting how they hire, it may be helpful to look at ways AI could improve your hiring process today:

Prescreening Candidates

Prescreening applicants demand more time and attention than perhaps any other task in recruiting. However, the days of manually reading through stacks of resumes and cover letters may soon come to an end.

There already exist search tools that allow recruiters to quickly surface candidates who meet specified criteria. These tools become much more powerful when AI is added to the mix. AI-enabled chatbots, for example, can prescreen candidates before a recruiter ever engages them. Through online conversations, chatbots can gauge candidates’ credentials, respond to their questions, and identify the most qualified applicants in the batch.

Scheduling

Simply scheduling interviews can be a major hassle, often requiring a lot of back-and-forth emails and days of wait time. According to our internal data at RoboRecruiter, it takes an average of 15 emails between recruiter and candidate to schedule one interview! Candidates grow frustrated when the process moves slowly, and this inefficiency can reflect badly on the company itself.

Inefficient scheduling and interview processes can have ramifications beyond a single candidate. Twenty-two percent of candidates who have a bad candidate experience will tell others about it, thereby driving potential talent away from the organization.

Many chatbots and other AI-driven solutions now offer automated interview-scheduling functions. Rather than exchanging endless emails, candidates can simply select interview times that work for them, and the AI tool will reserve time in the interviewer’s calendar.

Sorting Candidates

Fifty-two percent of recruiters feel the hardest part of recruiting is identifying qualified candidates in large applicant pools. AI can help recruiters to cut through the noise that often surrounds an open role.

Chatbots can engage candidates to pre-vet them before they ever make it to a recruiter. Based on these conversations, chatbots can identify the top matches, which means recruiters spend less time screening unqualified candidates. Chatbots can also fill in gaps in candidates’ resumes by asking relevant questions, ensuring that recruiters get a full and accurate picture of each prospect.

Keeping Candidates Engaged

Candidate engagement is a critical component of the recruiting process, and AI can be of great value here. Few recruiters have time to engage with candidates manually on a regular basis, but automated chatbots and other AI tools can do just that. AI can also be used to maintain relationships with candidates who are not yet available or right for a role at the company. AI can automatically reach out to these candidates when new roles open up, keeping them engaged with the company in the long term.

Writing Better Job Ads

A great deal of time is spent answering the same redundant questions about a role from candidate after candidate. AI can help head off these questions before they are even asked. By analyzing previous job ads and frequently asked questions, AI can make suggestions on how to update job ads to give candidates all the information they’re looking for.

The recruiting process can have a significant effect on a company’s future, but the journey from application to hire can be as time-consuming as it is costly. AI has recently emerged as a major tool that can both cut costs and save recruiters time through automation. AI is streamlining the entire hiring process, bringing the most qualified candidates to the right jobs faster and cheaper than ever before.