Career Networking

In the US, the multigenerational workforce is divided into five age groups, which have quite different approaches to employment.

The traditionalists (or silent generation, born 1925–1945), have these stereotypical characteristics: striving for financial security; "waste not, want not"; nobility of sacrifice for the common good; focus on quality and simplicity; loyal to employers and expect loyalty in return; believe promotions, raises and recognition should come from job tenure; work ethic focused on timeliness and productivity; conformity and following authority.

The baby boomers (born 1946–1964), have these stereotypical characteristics: the importance of hard-work (instilled by parents); loyalty to an employer would lead to reward and seniority; willingness to take on additional responsibilities; conscientious and dependable; service-oriented; ambitious; dutiful.

The generation X (born 1965–1981) have these stereotypical characteristics: the importance of education; shaping one's own career path; work-life balance and autonomy; innovation and entrepreneurialism; comfortable with challenging conventional wisdom; outcome-oriented; collaborative decision making.

The millennials (or gen Y, born 1982–1997) have these stereotypical characteristics: need intellectual challenge; entrepreneurial; value continuous learning opportunities; achievement / results-oriented; innovative and open to new ideas; collaborative decision makers; like praise and recognition; value teamwork and equality; value independence / autonomy; seek meaningful work; value work-life balance and flexibility; value fun at work; technology-driven

The centennials (or iGen or gen Z, born 1998 and later) are just entering the workforce and the stereotypes have not yet been formed.

The traditionalists rely on local organizations like the Rotary or the golf club for networking, but also professional societies and conference attendance. The baby boomers participate actively in international societies and conferences, building a global network. The generation X still participates in conferences but is less active in professional societies and the organization of conferences. The millennials are on social media and use search engines to find information and attend local meet-ups for networking.

While in the past peoples managed contacts using a Rolodex, membership directories, etc., today colleagues are constantly on the move and everybody has to maintain their personal contact information on a professional network like Xing or LinkedIn, through which they connect to their professional contacts.

Professional network sites make money by selling your information to business intelligence and salespeople as well as recruiters looking for employees. The service is free for you, but you have to maintain your own information.

The sites are continuously improved, so you have to keep monitoring your profile for changes in the way your information is organized to be more valuable to paying customers. For example, the skills section is sorted by the number of endorsements you receive for each skill, which is not what you want. Edit this section by clicking on the pencil on the top right, then unpin the top three skills, reorder the skills by dragging the horizontal lines on the right, and pin your top three skills.

When LinkedIn bought SlideShare, your presentations appeared in the media section. However, the original site was abandoned and your media is in cold storage. To get acceptable access times, you have to upload your PDFs again directly into LinkedIn. Furthermore, videos are no longer supported, so you have to upload them to YouTube and then make them available in your LinkedIn profile as linked media.

If you apply to a job on a professional social network by clicking on the "apply" button, the probability that you will have that job in your profile is very low. Instead, you have to click on your best connection working there because jobs go mostly through internal referral.

Of course, you have to have a contact working there. The quality of the contact is important because this person has to be your advocate. You can easily increase your network by turning on Bluetooth in your mobile LinkedIn app and invite all people in your vicinity, but they will not be your advocates. Your network has to be dense.

The best way to create a dense network is to organize conferences because people will remember well your skills and leadership qualities. The second best is presenting at conferences and the easiest is to present at meet-ups. Even easier is to write a blog, but you should post at least once a week and advertise each post on LinkedIn and Twitter.

Zuckerberg did not get it

Formal written question to our Newell Road neighbor, Facebook CEO Mark Zuckerberg on Edgewood Drive:

Describe how your business philosophy distinguishes the harm to individuals from the harm to society.

The officially recorded answer for posterity:

We recognize that we have made mistakes, and we are committed to learning from this experience to secure our platform further and make our community safer for everyone going forward. As our CEO Mark Zuckerberg has said, when you are building something unprecedented like Facebook, there are going to be mistakes. What people should hold us accountable for is learning from the mistakes and continually doing better—and, at the end of the day, making sure that we’re building things that people like and that make their lives better.

Particularly in the past few months, we’ve realized that we need to take a broader view of our responsibility to our community. Part of that effort is continuing our ongoing efforts to identify ways that we can improve our privacy practices. We’ve heard loud and clear that privacy settings and other important tools are too hard to find and that we must do more to keep people informed. So, we’re taking additional steps to put people more in control of their privacy. For instance, we redesigned our entire settings menu on mobile devices from top to bottom to make things easier to find. We also created a new Privacy Shortcuts in a menu where users can control their data in just a few taps, with clearer explanations of how our controls work. The experience is now clearer, more visual, and easy-to-find. Furthermore, we also updated our terms of service that include our commitments to everyone using Facebook. We explain the services we offer in language that’s easier to read. We’ve also updated our Data Policy to better spell out what data we collect and how we use it in Facebook, Instagram, Messenger, and other products.

Obviously, he did not get it. A net worth of $77.6 billion does not make you smart. Rejoice, there is hope for you.

洋、お誕生日おめでとうございます。

New Swiss State Secretary for Education, Research and Innovation

Today, the Swiss Federal Council appointed Martina Hirayama as the new State Secretary for Education, Research and Innovation at the request of the Federal Department of Economic Affairs, Education and Research EAER.

Martina Hirayama has been president of the Institute Council of METAS, the Federal Institute of Metrology, since 2012. She has also been vice president of the board of Innosuisse, Switzerland’s Innovation Promotion Agency (up to the end of 2017 the Commission for Technology and Innovation) since 2011 and a member of the Swiss National Science Foundation’s Foundation Council since 2016. Since 2011 Ms Hirayama has been dean of the ZHAW School of Engineering and is a member of the ZHAW’s Executive Board. Since 2014 she has also been Head of International Affairs.

Martina Hirayama studied chemistry at the University of Fribourg, at the ETH Zurich and at Imperial College London, obtaining a doctorate in technical sciences from the ETH. She later took a postgraduate degree in economics at the same institution. Following her doctorate she was group leader at the Institute of Polymers at the ETH Zurich, from 1995. During this time, Ms Hirayama co-founded a start-up in new coating technologies, and was CEO of the company until 2008. In 2003 she began lecturing in industrial chemistry at Zurich University of Applied Sciences Winterthur ZHW, where she developed and headed the field of polymer materials and obtained her professorship. From 2007 to 2010 she developed the Institute of Materials and Process Engineering. Ms Hirayama is a citizen of both Switzerland and Germany.

With such wide-ranging experience in research, teaching, entrepreneurship, management and administration, Ms Hirayama is very well equipped to head the State Secretariat for Education, Research and Innovation SERI. She has impressive expertise at the interface between science and business. The Federal Council has chosen a person with huge initiative and creativity, with a broad network in the field of education, research and innovation as well as politics, public administration and the private sector.

Ms Hirayama perfectly meets the exacting requirements of this position of State Secretary for Education, Research and Innovation. The important task of equipping Switzerland’s excellent ERI system for the digital future falls to the state secretariat she will now head. The Confederation, cantons, professional organisations and other players must work together to continue to strengthen both vocational and professional education and training and academic education, and to maintain Switzerland’s position as a world leader in research and innovation.

Creative Professions

The Silicon Valley has seen radical changes in how people work. By people, I mean mostly the creative professionals who conceive the products that made the valley famous. Today, these professionals are not as creative as in the past. We are transitioning to the gig economy, where professionals do not have a fixed job but use the internet to find small assignments. The pay is very low, there are no benefits, and the money is all made by the service website owners: all work is purely transaction oriented and in the case of software, when an app breaks, it is simply abandoned.

There are conventional jobs with an employment contract and benefits, but the setting is more that of factory workers doing piecework controlled by the company's GitHub site. The companies do not invest in their workers, which do not learn new technologies and hop to a new employer every couple of years.

The gig economy is different from consulting. Consultants earn approximately twice the salary of a regular employee and make a considerable investment to deepen their expertise.

In the past, an employee was a resource groomed by companies. The new trend is the reason the most creative products now come from outside the Silicon Valley. The new centers for innovation include (from west to east) London, Lausanne, Zurich, Berlin Beijing, Shanghai, Taipei, Seul, Tokyo, …

Before the transition to piecework, a paradigm popular in the Silicon Valley was that of the field dependence of cognitive styles, going back to Herman Witkin in 1962. This paradigm was used to give employees work in which they could excel, form powerful synergistic teams, and also to design user interfaces.

People with a field dependent cognitive style, are driven by an inner motor (god). They think in a global context and tend to think in parallel, making associations. Field-dependent employees often work well in teams, as they tend to be better at interpersonal relationships. When designing user interfaces, approaches that connect different parts of a topic are useful for field-dependent learners. For example, users can discuss what they know about a topic, predict content, or look at and read related material.

People driven by a field-independent cognitive style are driven by an outer motor, for example, the product's user. They are analytical, detail oriented, and tend to think sequentially, drawing inferences. Field-independent workers tend to rely less on managers or colleagues for support. In user interfaces, approaches such as extensive reading and writing, which users can carry out alone, are useful.

Research labs looked for employees that have both a field dependent and a field-independent cognitive style. Such people can envision new theories and can also reduce them to practice by implementing them. Such an activity is called speculative design.

This paradigm can be extended to the pieceworker of today, who is driven by greed (self). It is also useful to extend the idea to other activities, as shown in this diagram:

Data Analysis Careers

On 25 April 2018, the European Commission increased its investment in AI research to €1.5 billion for the period 2018-2020 under the Horizon 2020 research and innovation program. This investment is expected to trigger an additional €2.5 billion of funding from existing public-private partnerships, for example on big data and robotics. It will support the development of AI in key sectors, from transport to health; it will connect and strengthen AI research centers across Europe, and encourage testing and experimentation. The Commission will also support the development of an "AI-on-demand platform" that will provide access to relevant AI resources in the EU for all users.

Additionally, the European Fund for Strategic Investments will be mobilized to provide companies and start-ups with additional support to invest in AI. With the European Fund for Strategic Investments, the aim is to mobilize more than €500 million in total investments by 2020 across a range of key sectors.

With the dawn of artificial intelligence, many jobs will be created, but others will disappear and most will be transformed. This is why the Commission is encouraging Member States to modernize their education and training systems and support labour market transitions, building on the European Pillar of Social Rights.

The annus mirabilis of deep learning was 2012 when Google was able to coax millions of users into crowdsourcing labeled images. They also had tens of thousands of servers that were not very busy at night. Most of all, however, Google has an incredible PR department that was able to create a meme.

1. Software defined storage (SDS) on commodity hardware made it very inexpensive to store large amounts of data. When the cloud is used for storage, there are no capital expenditures.
2. Ordinary citizens became willing to contribute vast amounts of data in barter for free search, email, and SNS services. They were also willing to label their data for free, creating substantial ground truth corpora that can be used as training sets.
3. High-frequency trading created a market for GPGPU hardware, resulting in much lower prices. Also, new workstation architectures made it possible to break the impasse caused by the end of Moore's law.
4. ML packages on CRAN made it easy to experiment with R. Torch and Weka made it easy to write applications capable of processing very large datasets.

Many companies are setting up analytics departments and are trying to hire specialists in this field. However, there is great confusion on what the new careers are and how they are different. Often, even the companies posting the job openings do not understand the differences.

Recently, in the Sunnyvale City Hall, two representatives from LinkedIn and a representative each from UCSC Silicon Valley Extension and California Science and Technology University, participated in a panel organized by NOVA, dispelling the confusion.

Essentially there are three professions: data analyst, data engineer, and data scientist:

• Data analysts tends to be more entry level and do not necessarily need programming or domain knowledge: they visualize data, organize information and summarize data, often using SQL. Essentially, they deal with data "as is."
• Data engineers do what is called data preparation, data wrangling, or data munging. They pull data from multiple, distributed (and often unstructured) data sources and get it ready for data scientists to interpret. They need a computer science background and should be skilled with programming, Hadoop, MapReduce, MySQL, and Spark.
• Data scientists turn the munged data into actionable insights, after they have made sure the data is analytically rigorous and repeatable. They usually have a Ph.D. The ability to communicate is vital! They must have a core understanding of the business, be able to show why the data matters and how it can advance business goals and communicate this to business partners. They need to convince decision makers, usually at the executive level.

Stanford Workshop on Medical VR and AR

5 April 2018, there will be a public workshop on medical head-mounted displays in Stanford. The workshop is designed to support collaborations between the engineers who are developing VR and AR technologies and the surgeons and clinicians who are using these technologies to treat their patients.

The workshop features talks by researchers who are developing VR and AR technologies to advance healthcare and panel discussions with Stanford physicians who are using VR and AR applications for surgical planning and navigation and for alleviating pain and anxiety in their patients.

There will be an interactive demo session featuring research projects, clinical applications, and startup ventures.

Seating is limited, so if you wish to attend, we recommend that you register now at the website https://scien.stanford.edu/index.php/medicalvrar.

Claudio Oleari

On 23 January 2018, Claudio Oleari passed away at the age of 73 in Reggio Emilia. He was the last and ultimate authority on the OSA-UCS color space and perceptually uniform color.

He was an eminent physics scholar and an associate professor at the University of Parma, at the Department of Physics and Earth Sciences. He devoted his life to the activities of teaching with the same passion and interest that he dedicated to research in the context of color, applying physics to perception and establishing its role in colorimetry. In 1995 he started the Gruppo in Colorimetria e Reflectoscopia, which later became the Associazione Italiana Colore.

His availability for colleagues and students and his ability to listen and advise are proverbial: his kindness will always be remembered by everyone who has known him. These qualities are exemplified by the message on his profile at the University of Parma “You are welcome any day and at any time, even without an appointment. It is useful to verify by telephone my presence in the office. To book a meeting and ask questions, sent an email to claudio.oleari@fis.unipr.it.”

He initiated, within the Italiana Association, many valuable informational activities and forged many connections which persist as a rich bibliography, always having in mind the need to invest in research and training both in Italy and abroad.” He initiated, within the Italiana Association, many valuable informational activities and forged many connections which remain as a rich bibliography, always having in mind the need to invest in research and training both in Italy and abroad.

His death leaves a void difficult to fill, and the world of color loses an intellectual and an attentive and informed scholar.

Perceptual Similarity Sorting Experiment

If you have an extra ~5 minutes please try out our online perceptual similarity sorting experiment.

This is follow-up to the work that Michael Ludwig (one of our summer interns from last summer) conducted and is continuing to work on as part of his PhD research.

For more details, please see this about page for the experiment. Thank you.

Annotating detected outliers

The so-called Twitter Anomaly Detection function for R is excellent but also very minimalistic. The input is a two-column data frame where the first column consists of the timestamps and the second column contains the observations. In addition to a plot, the output is a data frame comprising timestamps, values, and optionally, expected values.

In practice, we usually have some semantic information that we would also like to include in the output, so we do not have to refer back to the original data. Fortunately, there is a quick-and-dirty way to add a description to the outlier data frame.

We start with the annotated data frame containing at least columns with the timestamps, the observations, and factors providing contextual or semantic information on each observation. We then create a simple data frame with just the first two columns, which we pass to the outlier detection function.

We can write a trivial function that for each outlier finds the row index in the simple data frame and looks up the semantic information in the annotated data frame:

AddDescription <- function(series1, series2, outliers) {
 quantity <-  lengths(outliers$anoms[1])
 if (quantity < 1) return (NULL)
 else {
   result <- NULL
  for (i in 1:quantity) {
   rowIndex <- which(series1$timestamp == outliers$anoms$timestamp[i])
   newRow <- data.frame(outliers$anoms$timestamp[i],
    outliers$anoms$anoms[i],
    as.character(series2$note[rowIndex]))
   result <- rbind(result, newRow)
  }
  colnames (result) <- c("timestamp", "outlier_value", "description")
  return (result)
 }
}

This function is just an elementary example. It is easy to add to each outlier more detailed information you can compile from the full data frame.

outliers with descriptions

	timestamp	outlier_value	description
1	2017-01-17 06:53:00	209	gear display flashing
2	2017-09-19 09:10:00	206	gear shift failure
3	2017-11-17 07:26:00	211	check engine lamp on

Dates are a sore point of analytics: they alway get you. When no time zone is specified, i.e., tz = "", R assumes the local time zone. In the data frame returned by Twitter's AnomalyDetectionTs functions, the time column has UTC as the time zone. Therefore, the following statement is useful after the call to AnomalyDetectionTs:

anomalies$anoms$timestamp <- as.POSIXct(anomalies$anoms$timestamp, tz = "")