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15 Global challenges facing humanity
15 Global challenges facing humanity
knowledge gives us the capability to see around corners.
As decision coaches, we maximize outcomes and minimizing unintended consequences. With that in mind we keep a close eye on the UN initiative the Millenium Project. By looking at the illustration below you will find at number 5 that Global Foresight and Decisionmaking is one challenges we need to improve on.
Regardless if the decisions we make might have a global impact or not, we still need to apply a good methodology and toolset to become better at decision making. This means we need to tap into the collective intelligence, together we know more. We would need to work in a way that utilizes our brain power i.e more visually, describe to decision in a model i.e. as in CAD, Architecture toolsets and simulate the impact today's decisions will have tomorrow. Working visually has many benefits, it truly simplifies understanding and our brain processes images 60.000 times faster than text and numbers.
Using a suitable methodology and supported toolset (as above) we will not only make better decisions, but we will have captured the collective knowledge in an executable visual model, documented not only what the decision was, but why we made the decision and to provide transparency.
A decision model has a significant benefit. It captures institutional knowledge that can be refined as we learn more, which we always need to do. As Charles Darwin put it. "Learning is the mechanism evolution has given us to cope with an environment in constant change"
Welcome to a world of better decisionmaking.
By, Göran Källmark
Looking Beyond The Negative Connotations
Develop The Mindset of The Apiarist
Whenever someone perceived by his peers as honest and reliable suddenly decide they want to “run for office ” and become a “career politician” , the chances are that four out of five persons will instinctively react negatively to this announcement. Why is that so? For the simple reason that a majority of politicians make promises they cannot keep.Hence they adopt an attitude of mistrust as a cautionary weapon to ward off any disappointments from any unfulfilled political promises. Every time we append negative connotations to everything that has to do with politics and politicians, we should do well to remember this parallel: The apiarist (beekeeper) is well aware of the painful sting from the bees, that does not stop them from daring further into the beehive to harvest its precious honey for food and medication.
In discussing the 5 Characteristics of Politically -Skilled Leaders, Phillip Braddy convinces us that these skills come handy no matter what profession you find yourself in. So instead of distancing ourselves from anything that reminds us of “Politics , Politicians ” and any other negative connotations thereof, why not immerse ourselves into the same playbook and learn the same skills which we the “apolitical” can put to great use?
By paying close attention to each of the 5 skill and the key lessons discussed in Barry’s article below, every aspiring for Leadership roles within their organisation shall benefit from the same playbook. And the fun side? You get all these benefits without falling in love with Politics!
5 CHARACTERISTICS OF POLITICALLY-SKILLED LEADERS
September 30, 2016
Think of a leader who you would consider to be “political” in your organization.
What words would you use to describe this leader? What images come to mind?
My guess is that you may have thought of adjectives such as “self-serving,” “manipulative,” “deceptive,” or “untrustworthy.” Or maybe this question prompted you to visualize images of secret pacts being made behind closed doors.
Conversely, you may have thought of more positive words like “influential,” “well-connected,” “resourceful,” or “socially astute.”
While the term political skill, or political savvy, generally elicits more negative than positive perceptions, it isn’t inherently a bad thing.
In fact, leaders can utilize their political skill to create positive outcomes. They can use it to successfully meet their organization’s leadership challenges and to improve the performance and productivity of their work teams.
Leaders can also use these skills to enhance their own, and perhaps their direct reports’, chances of career advancement.
If leaders want to become more politically savvy, Gerald Ferris (a professor of management and psychology at Florida State University) and colleagues have demonstrated that they’ll need to master 4 skills. In addition, researchers at CCL and Davidson College recently expanded Ferris’s typology to include a fifth skill.
All 5 political skills are described below.
Social Astuteness – the ability to observe others and to accurately understand them. Socially astute leaders are good at reading people’s non-verbal behaviors and can intuitively sense the motivations of others.
Interpersonal Influence – the ability to influence others using a compelling interpersonal style. In particular, leaders with strong interpersonal influence are good at establishing rapport with others, they communicate well with others, and thus, they are also good at getting others to like them. Getting others to like them, in turn, helps them influence others more easily.
Networking Ability – the ability to establish relationships with others. People with high networking ability have strong ties with many people, including influential people at work. They are particularly skilled at leveraging their networks to obtain the needed resources to accomplish both personal and organizational tasks.
Apparent Sincerity – involves being transparent, honest, and sincere with others. Leaders with apparent sincerity believe their word is their bond – they do what they say they will do.
Image Management – the ability to intuitively know what to say to influence others and knowing how to make a good impression on others.
How do we know that these individual political skills really matter?
Most research to date has examined the relationships between overall political skill and leader effectiveness, but it has not examined the associations between each of the 5 individual political skills and effectiveness.
In a study conducted by researchers at CCL and Davidson College, we addressed this knowledge gap by finding that leaders who had greater social astuteness, interpersonal influence, networking ability, and image management received higher leader effectiveness ratings from their bosses than leaders who scored lower on these political skills.
Apparent sincerity was not found to be important for men, but women with higher levels of apparent sincerity were perceived to be more effective leaders than women with lower levels of apparent sincerity.
Put differently, as unfair as it may be, women are penalized when they exhibit low sincerity, whereas men are not.
Of the 5 political skills, image management and interpersonal influence are the most important for helping a leader to have more impact at work.
Finally, image management is more important for middle managers as compared to upper-level managers. By contrast, interpersonal influence is more important for upper-level leaders as compared to middle-level leaders.
Key Lessons?
In addition to having traditional managerial skills in areas such as budgeting, planning, coordinating, and the like, all leaders need to also possess political skill. They especially need to be adept at making good impressions and exerting interpersonal influence on others.
Women leaders may also want to pay particular attention to whether or not they are perceived as being genuine and sincere.
Possessing these political skills should enhance leaders’ effectiveness at work, improve their team’s performance, and improve their own chances of career advancement.
The content of this blog is based on the following articles:
Braddy, P., & Campbell, M. (March 2013). Using political skill to maximize and
leverage work relationships. Center for Creative Leadership: Greensboro, NC.
Ferris, G. R., Treadway, D. C., Kolodinsky, R. W., Hochwarter, W. A., Kacmar, C. J., Douglas,
et al. (2005). Development and validation of the political skill inventory. Journal of
Management, 31, 126-152.
Snell, S., Tonidandel, S., Braddy, P. W., & Fleenor, J. W. (2014). The relative importance of political skill dimensions for predicting managerial effectiveness. European Journal of Work and Organizational Psychology, 23, 915-929.
By Tambe Harry
TameFlow - A brief Introduction
TameFlow - A brief introduction
What is all the fuzz about?
Why is this important?
Well, it comes down to a good decision capability. That might sound a bit obvious, but the truth is all there. For good decisions to be made it is important to understand that we all need to be in a constant learning mood. Why? The world around us is in perpetual change and hence we need to be equipped to understand the various actions which lead to the desired outcomes and trying to avoid the unintended consequences. That is what learning is so important.
Where does the knowledge reside?
We can find it in a huge amount of various sources, but we usually find contained and cognitively used by us humans, where different experiences and insights lead new knowledge. We as a collective group represent knowledge, insights, and experience. This is known as collective intelligence or collective knowledge. To take advantage of this over time it is important to keep challenging each other and continuously refine our collective knowledge base.
The writer Maria Popova put it eloquently in the following quote.
"Allow yourself the uncomfortable luxury to change your mind..."it is infinitely more rewarding to understand than to be right."
How do we get there?
For this to happen, a culture where each and everyone is open to being challenged and encouraged to challenge is needed. Corporate culture is established through behavior and behaviors originates from values. To have any real impact, the setting of the culture need to come from the top, and only by the leadership acting according to the values. This is very much when true leadership is required. The culture should encourage learning, experimentation, and be tolerant of mistakes. A concept that allows an organization to flourish is the double loop learning. It means that you do not only look on how to improve what you are doing but also how it is being done, i.e., can our processes improve.
This type of culture encourages communication which is vital for teamwork, and it benefits sharing knowledge and the gaining of insight with a clear focus on the goal ahead. This enables authority to be delegated, and decisions taken where and when it needs to be
In a team or organization being based on the noble patterns "unity of purpose" and "community of trust", meaning we all know and share the purpose of our contribution, and harness the power of collective intelligence. As a consequence better & faster decisions are being made leading to improved performance. Next, we identify the biggest bottlenecks in our workflow and jointly focus on solving them. All will benefit, and the goal will be reached through a refined and more efficient way of working. The new refined way of working will continue to be improved upon for higher levels of productivity.
Göran Källmark. [email protected]
Engage the fox - A fable on the theme of collective intelligence
It is a book wich use various animals to illustrate the various qualities we have, and in essence than critical thinking and teamwork take you a long way.
No Woman Should Chose between her Education and her Child.
If you are a woman and mom to a toddler, then you agree with me on at least one of the following:
Your public life takes a dive because people make you feel embarrassed to freely live your life as a mom in public;
You put your career and education on “hold” because the policies at school and workplace say these are not the places for your crying toddler;
You are crippled by society’s imposition of “ethics” that deny both mother and child their rights of access to each other;
But.....
Listen up...
The actions of one man might just be the beginning of the change you have been waiting for. That man is Professor Sydney Engelberg
http://ow.ly/Y73qr
Insights into the concept of " The World Resource Sim Center"
If the concept of Dr. Lorien Pratt's “The World Resource Simulation Center “ should come across as too complex to decipher, we are confident the three paragraph below shall provide you the key to unlock and enjoy the treasures of this article.
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When people come together in groups to “see” or “visualize” problems or data that represent these problems, the results can be very spectacular. By using virtual agency to “immerse” themselves into and gain direct access to remote and wicked problems from around the world, Decision Intelligence Experts avail themselves of other peoples’ problems and experiences in a unique and empathetic way.
They no longer approach the problem as uninformed and unaffected “outsiders”. As they deploy “group thinking” within this new environment of virtual reality, they brainstorm for solutions around the problem as informed experts who know exactly what viral strain of the human predicament they are dealing with.
Through the cross-fertilization of ideas, they simulate models of real life situations from these visualizations that make it possible for them to discover solutions to complex problems. The concept of the Sim Center makes a bold statement to emphasize that a combination of advanced technology and human collaboration is what Society needs to solve the World’s most wicked problems.
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The World Resources Sim Center: on its way to Silicon Valley
January 4, 2016
Complex SystemsDecision intelligenceEnvironmentIntelligence Augmentation (IA)Leadership for decision makersSimulationWicked Problems
Last month I received an intriguing email inviting me to an event at Kimberly Wiefling’s house. I’d met Kimberly before through Jonathan Trent, as part of the work I’ve been doing to help out the Omega Global Initiative. I knew she was an international consultant, but it was great to also learn that she was passionate about systems thinking and visualization. Jonathan and I drove up to Kimberly’s house together, where she and Peter Meisen explained their initiative to bring a Buckminster Fuller-inspired Sim Center, based on a similar center in San Diego, to Silicon Valley.
The idea, says Kimberly, is that Buckminster Fuller laid out a vision for transforming the world through spontaneous cooperation. To realized this vision, people need to work together to make decisions, so having a visualization center where they can visualize data about the world is very helpful in group collaboration.
The San Diego facility provides “immersive visualization”, which physically surrounds participants with the issues, trends, and future projections relevant to firms and organizations. The SimCenter offers a meeting space to facilitate collaboration amongst an organization’s stakeholders (e.g. staff, suppliers and clients), working in teams to visualize concepts, problems, and opportunities, and then reporting to the group.
I spoke with Kimberly today to understand her current plan. She told me that “We’ve been doing a pilot in San Diego with a 4,000 square foot facility for the past 4 years. We think it’s time to have a facility to address wicked problems here in Silicon Valley. The reason: In the same way that today Silicon Valley is an example of entrepreneurship that’s admired and copied globally, we intend for Silicon Valley to become a global example of a sustainable region that inspires other regions to transform themselves in the area of sustainability.” In her own consulting work, Kimberly takes Silicon Valley innovations in entrepreneurship to Japan and other countries.
Kimberly went on to say that she sees the valley as a model: “We want Silicon Valley to be a global example of a sustainable regions that the world can learn from, so as to create a global community, with the visualization center serving as a magnet for people to come together and share their vision.”
Kimberly and the Sim Center’s vision overlaps deeply with Decision Intelligence. DI and the Sim Center are complementary approaches to solving the same problem: massive “wicked” problems that require us to understand interdependencies, to re-synthesize what are otherwise stovepipes of information and initiative. Time and again, I see solutions that are limited because they don’t take interactions into account. The best example was a telecom CIO I met with once who told me: “We’ve optimized the KPIs in every department, but the company is still struggling.” Why? Because optimization of a complex enterprise requires an understanding of the “whack a mole” linkages that are often hidden under the table: we improve in one area and it negatively impacts another. Instead, working together in initiatives like the Sim Center aids a broader understanding. And simulation exercises are exactly the right approach to pull it all together.
So I’m flying to San Diego too, leaving tomorrow (Tuesday) night. Along with 15 other passionate advocates of the Sim Center approach, we’ll be collaborating in a workshop to learn the approach and to bring it to the valley. My own goal is to facilitate the inclusion of Silicon valley technology such as great UX design, machine learning, massive data analysis, sensors, and more into an integrated approach that unifies the best of advanced technology and human collaboration.
Kimberly tells me that the plan is to create a prototype temporary location at first, then to learn from it, revise as needed, then to create an iconic bricks-and-mortar facility in which local students, community governments, and businesses can work together to effect change.
Please drop me a line in the comments with any steering thoughts or resource ideas that could help the initiative. And I look forward to seeing some of you in San Diego!
Employ Your GPS Mindset in 2016:
No condition they say is permanent. If your organization is known to deploy a one- size-fits- all strategy in the past, there is no guarantee that the same strategy shall work every time. You might drive South on a bright morning , but on your way back storms cause trees to block the only path that you know. Thankfully your car has a GPS system to guide you so that you do not get lost. The same can be said of CEOs and Managers as they face new challenges. The following article by Dr. Mary Lippitt is a timely reminder of what every decision maker must do to survive in today's uncertain business environment.
Guest post: Mindset GPS: Navigating New Realities
January 1, 2016
Guest PostInnovationLeadership for decision makers
Great leaders make the right call at the right time to deliver outstanding results. They avoid relying on outdated mindsets and practices in a complex and changing environment. Leaders today must be willing to help others to think strategically, question past practices, and explore new alternatives.
Relying on old habits, acquiescing to group think, and depending on obsolete assumptions limits individual careers and reduces organizational viability. A painful example: in the 1990s, mortgage bankers granted 95% mortgages based on the wrong assumption that home prices never fall more than 5%. They paid a high price for their narrow thinking. Additionally, they ignored expert warnings about a real estate bubble. One bank executive stated that he knew it would blow up, but as long as the music was playing, he had to keep dancing. Instead of searching for a new melody, he went along for the ride.
The bedrock of intelligent strategic thinking consists of carefully assessing environmental signals from multiple perspectives to uncover new alternatives. A wider information filter is necessary to collect information, explore new alternatives and assess costs and benefits. My book, Brilliant or Blunder: 6 Ways Leaders Navigate Uncertainty, Opportunity and Complexity, presents a checklist of six mindsets that are designed to ensure situational awareness before jumping into action or pursuing the same well-worn path. The mindsets cover information in the following areas:
Inventing: Developing new products/services and capturing synergies
Catalyzing: Focusing on enhancing customer service and competitive position
Developing: Providing better infrastructure, policy and organizational systems
Performing: Improving quality, ROI and workflow
Protecting: Creating a high-performing culture and retaining key talent
Challenging: Identifying trends, new niches and more effective business models
While leaders may want to pursue all six mindsets simultaneously, the leadership challenge is in selecting the one or two areas that are most critical at this point in time. It is tempting to implement more than two, but it is unrealistic from a management perspective. When “faster, cheaper, and better” became a popular mantra people recognized that only two of these three goals were possible. Leaders must make the hard choices to pursue the more relevant path to organizational success.
Staying on track to ensure success requires mental agility in making smart and timely trade-offs. When detours and barriers arise, mindsets provide guidance like a GPS system correcting us when we veer off course. These six mindsets can also be used to “recalculate” our decisions to ensure that we reach our desired destination. And just like our GPS navigation systems, leaders must employ strategic thinking to leverage current reality.
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Mary Lippitt
Founder at Enterprise Management Limited
Dr. Mary Lippitt is an internationally recognized leader in strategy thinking, change execution, and leadership effectiveness. She is the author of the award-winning book Brilliant or Blunder: 6 Ways Leaders Navigate Uncertainty, Opportunity and Complexity. Find out more her new book at the Brilliant or Blunder website (http://www.brilliantorblunder.com) Lippitt founded Enterprise Management Limited 30 years ago to help leaders make smarter decisions for better results. She developed the Leadership Spectrum Profile®, which was awarded one of the Top Ten Training Products in 2000 by Human Resource Management Magazine.
Which does man fear most? God or Gun?
Steps to think through before you hire a machine-learning consultant.
As a busy manager, you want to get things done in the best way that impacts the outcome of your business and organization. The decision to hire a machine-learning consultant tasked with delivering "just in time" solutions requires that you pick the right person whose input shall keep your organization performing at its highest levels.
In the following article "How to hire a machine-learning consultant" , Lorien Pratt provides some very useful suggestions and principles that should guide you. Isn't it heart-warming to know that our Decision Intelligence Experts "have your back" in your quest to make that hire that would change the future of your business for the better?
How to hire a machine learning consultant
December 5, 2015
Machine LearningIntroductionArtificial Intelligence
More and more organizations are realizing the tremendous benefit of machine learning to their bottom line, yet many are not ready to hire a full-time machine learning expert. So a machine learning contractor/consultant/freelancer makes sense.
For instance, your business might depend upon “just in time” delivery of some goods, and you need to predict when customers will run out. Or your municipality might be looking to understand what decisions will lead to your sustainability goals. Or you want to learn the signals that customers send when you’re about to lose them. Or build a visual recognition system to detect faulty parts on your assembly line.
For the first time in the last year or so, the decreasing price of computing resources, coupled with an explosion in machine learning performance, means that there’s a great potential for business outcomes that comes from applying machine learning to your data.
If you’re considering this route, there are a few important principles to keep in mind.
Before the project
Most of my clients start out with a pretty clear idea of what the machine learning project should produce. I’ve found, however, that a few important questions often go unanswered. Be sure to figure these out ahead of time.
Are we looking for a person to develop an entirely new machine learning algorithm, or to apply an existing algorithm to our problem? This is like asking “Do you need someone to design a new car, or to drive an existing one?” Since many machine learning experts come from academic settings where the focus for many years has been on designing new machine learning algorithms in the first place, the skill of “driving” has been a bit overlooked. So be sure to look for a consultant who knows what it means to match the right algorithm to your problem, and then to do the data management, parameter tuning, and training work to get it built.
How does this machine learning algorithm fit into our business as a whole? A clear understanding of your business context—often expressed as a process or data flow—helps your consultant to know whether to tune for true positives or true negatives, how often new data is available, what performance level is “good enough”, and more.
Is this a fully automated or human-in-the-loop situation? If your system will be processing thousands of data items every second, without human intervention, then you’re going to build a very different system than if a key requirement is that humans can understand (and so override or supplement) its logic. If the latter, then that’s what decision intelligence is all about.
How far towards deployment do we want our contractor to go? They can just build the core algorithm, and deliver it in R (or SAS or SPSS), which might then need to be converted to a deployment language by your developers, or they can do that conversion, embed the algorithm in an application, and even go as far as to develop and deploy the application in a big data environment. There are different areas of tool expertise required for each step of this process, and it’s rare to find someone expert in all of them.
Is our data ready? Is your consultant responsible for cleansing, validating, etc.? Some consultants, especially those who come from a data science or database background, are quite good at this. Some not so much. Most machine learning systems use quite simple data formats, such as a single .csv file, so you’ll need to get it ready for them. Usually, I’ve found that data preparation is more cost-effective to do with data management expert resources than a machine learning person.
Typical project steps
A typical machine learning project follows some subset of the following steps:
Agree to how success will be measured. The simplest here is a simple holdout test set. Lately, though, I’ve had a few clients who say that success also depends on how understandable the system behavior is (e.g. recognizable visual features or a decision tree that makes sense and can be trusted). Since most good machine learning algorithms are “black box”, it’s good to ask.
Select the machine learning algorithm to use, based on the problem. The most common algorithm categories are classification, regression, and recommendation. If needed, develop a new algorithm. But keep in mind, this can be expensive!
Agree to how the algorithm will be tuned. For true-positives or true-negatives? Top-10 better than baseline top-10? Straight sum of squared errors?
Agree to any feature engineering. This step is often overlooked, yet good feature engineering can make a big difference to performance. Though there have certainly been successful projects using “raw” data, your subject-matter expertise in how the data might be modified to best find patterns can really help. Your machine learning expert should be able to collaborate with you on this step, helping to elicit your expertise in this regard.
Prepare the data. As above, often this means converting multiple tables to just a single one. The “trap for young players” here is that data preparation time is often much larger than originally expected. I recommend you make a careful estimate, then double it.
Choose the machine learning platform tool.
Run a baseline. This is an important step that’s often overlooked. I’ve often found that, especially with good feature engineering, a simple classifier can produce great performance. This is also a good way to test your success measurement method.
Do the learning. This is the core of the project. Pivot and grid search as needed on parameters, classifier size, and even algorithm, as needed.
When done, report results. If possible, produce a fabulous visualization of the results. If you’re ambitious, visualize learning in process and/or the inside representations.
Integrate the algorithm into a production environment. In many circumstances, this involves re-implementing it in code (e.g. if it was originally developed in R or SAS).
Integrate the algorithm into a “big data” production environment, using cluster, GPU, or other high-performance compute and data storage / management resources.
Now, I’ve never had a project that required all of these steps. A typical contract is around three to five of them. So treat the list as a menu of options as you decide the scope of your project.
How well do you know the tools of your trade?
A bad worker will always “quarrel with his tools” and provide excuses to customers when he fails to deliver. It would help his future relationships with his customers if he can identify the exact nature of the problem and invest himself to seeking solutions to these. On the other side a worker with a sound knowledge of the tools of his trade shall put these to effective use. The high efficiencies and subsequent high customer satisfaction attest to a smart work culture that comes with knowing the tools of one’s trade. Follow Lorien Pratt as she discusses five tool categories within Machine Learning.
Machine learning tools of the trade
November 30, 2015
Machine LearningTechnology analysis
Like any maturing discipline, machine learning is splitting into specialties. And just as a surgeon uses a scalpel, and a general practitioner prefers a stethoscope, different tools are appropriate for different use cases within these subfields.
In the last few months, I’ve run projects that have used tools in at least five categories. Here’s a roundup:
Tools for machine learning researchers: Examples here are Theano, Caffe, and Torch. Designed at universities, these tools retain their roots there, with documentation assuming you’ll be willing to learn the math and algorithms of machine learning. Getting them up and working takes, in my experience, several days at least, for a fairly advanced programmer / sysadmin type. This is a big investment of time, which is well justified if you’re already up the prerequisite learning curve and will be using this tool extensively, especially if you’re looking to build cutting-edge algorithms.
Tools for non-researcher data scientists: Machine learning for AWS and Microsoft’s Azure Machine Learning are targeted at the newly emerging data science specialist. This expert is not an algorithm designer nor a PhD student, but rather a practitioner who wants to build learners, fast. Just as this role is brand-new, so are these tools, with both announced in the last few months. Here, the prerequisite learning curve is much smaller, and the tool learning time also shorter. These visual environments make it particularly easy to learn, too. But you’ll hit a point where you’ll want to advance to the professional tools
Tools for advanced professional ML “drivers”: Statistics overlaps substantially with machine learning, but the tool set is much more mature. Tools like SAS, SPSS, and (more recently) R are standard issue in the professional statistician’s kit.
Designed for the trained professional, the investment required to learn the underlying statistics, and also these tools, was historically quite substantial.
But a funny thing’s happening with the new kid on the block: R. First, it’s is gaining strong foothold against SAS in even the most advanced modeling institutions: places like banks, mortgage companies, and more. Second, if you’re willing to work in a text-based world (which isn’t hard once you get into it and let’s face it, REPL can be fun), new ML libraries mean that you can build sophisticated learners really fast, and without a big learning curve.
An example: on a recent project, I and a colleague spent five days together trying to get Caffe to work for a deep learning problem. After a lot of frustration and an unnecessarily complexified network specification language, we pivoted to H2O, and had it running in about 20 minutes. This experience was typical of a number of recent projects of mine, where H2O was an order of magnitude easier to use. Bottom line for me:
I love #H2O, and use it whenever I can. It’s faster and more nimble by far than anything else.
CLICK TO TWEET
Tools for scale and speed: I’ve dipped my toe into Mahout lately, which is in an entirely different category than the others. Once you’ve built your learner (using one of the above approaches), in many scenarios you’ll want to deploy it to process massive amounts of data. Running Mahout on an AWS EMR cluster, for instance, you can stand up a substantial virtual data center in minutes at low cost, and crunch data at scale. This is where cluster compute ML environments like Mahout and Apache Spark MLLib come into their own. And the flops you’ll get on AWS is virtually limitless including (unlike H2O) GPU-based machine images, which will scream your ML-based systems into hyperdrive.
As with the other professional tools, you’re going to need a strong technical background. Here, however, it’s more of the *nix sysadmin variety, combined with some data wrangling expertise, rather than ML algorithm or software development.
Roll your own: Finally, don’t forget to consider rolling your own tool when needed. Most machine learning algorithms are reasonably simple to implement from pseudocode, and are openly described in technical articles, many of which have been published in open-source repos, which (modulo licensing: be sure to check) an average coder can modify and extend for special needs. This is the approach I took recently when I needed a restricted Boltzmann machine without the kerfuffle of an academic framework around it. After all, the core algorithm is less than 50 lines of code. I found a basic github repo to fork, and added a bunch of functionality to meet my client’s needs.
Have I missed any categories and/or tools? Please add your thoughts to the comments and I’ll include the best in a later draft of this article.
What Do We Learn From History?
If Great Leaders make informed Decisions, and Informed Decisions make Great Leaders, would it be stupid to suggest that uninformed Decisions could diminish and tarnish the reputation of even the Greatest Leaders? In view of the recent world events that grieve us all, it is necessary that those in decision making positions exercise Great Leadership and restraint. Great Leaders would take a moment to consider the unintended consequences of their actions. Fair enough, we can argue that no decision has ever been without unintended consequences. but does the inevitable "collateral damage" justify our response to violence with further violence? Granted, these are very complicated situations that require insightful dispositions. While there is no textbook answer on how to best handle these situations, it is wise to remember that Humanity as a whole shall benefit every time we break down walls and build more bridges that connect us . What exactly do we learn from history,and does doing the right thing make us powerless? In the words of Paul Rogers "Bad decisions are a bit like Trojan horses " : they crawl unannounced into our Political life, Business premises, and even into our Situation War Room and ruin everything .The following article by Paul Rogers which we are glad to share with you recounts the impacts of some bad decisions in history.
Bad decisions in history: Cautionary tales
History has been full of bad decisions. The Trojans brought the famous wooden horse inside their city walls, not realizing it was full of Greek soldiers who would open the gates from the inside. Napoleon decided to invade Russia and returned with just a tiny fraction of his once grand army. The Titanic was outfitted with only enough lifeboats for a third of the total passengers and crew it could carry.
What goes wrong with decisions like these? Sometimes it’s just individual arrogance or foolishness that produces a bad decision. But we’re organizational specialists, and we like to search out what’s amiss in the system that produces the decision.
For instance: One of the keys to good decision making is assigning responsibility for all the essential roles in a decision, from recommending a course of action all the way through to executing it. The recommender plays a particularly big part by gathering input from the relevant people and getting signoffs from anyone who needs to approve the proposal.
Then, too, a good decision process ensures that the right people offer input and are listened to. The Trojans heard from the priest Laocoön, the prophetess Cassandra and even Helen of Troy, all of whom cautioned that the horse might be a trick. Alas, no one paid attention. Laocoön was strangled by sea serpents for offering his opinion—another poor decision practice.
The fact is, great decision processes require many elements of an organization to work in concert. When we talk to executives, we often use a wheel-shaped graphic to illustrate all the factors that can come into play (see Figure 1). Every element that’s out of kilter is likely to compromise decision making and execution.
Take Napoleon’s army. Yes, the weather was one reason the invasion of Russia failed, but poor organization and Napoleon’s own leadership style were big factors. When officers reported supply shortages or desertions, Napoleon would give them a public scolding, often followed by a demotion. Predictably, Napoleon’s generals began exaggerating troop strength and readiness, obscuring the true picture until the campaign was far advanced.
Uncharacteristically, Napoleon lacked a clear vision in this case as well: Did he mean to occupy Moscow and Saint Petersburg? Carve up Russia between Sweden, Turkey and a revived Poland? He remained uncertain whether he would leave Moscow or winter there. By the time he decided to retreat, it was too late.
Getting business decisions right is tough, too. A company has to make good choices time after time. It has to do so speedily—faster than competitors—and it has to ensure that decisions get translated into action. No wonder there are as many missteps in this sphere as in every other area of history. Think of Coca-Cola introducing New Coke, or Polaroid and Kodak stubbornly sticking to film-based photography for way too long. (For more such blunders, see Huffington Post’s blog entry “The Worst Business Decisions of All Time.”)
When a company makes a really bad decision, it’s likely that more than one organizational element isn’t working right. The wheel can help you spotlight each individual trouble spot.
Whatever else it may have lacked, for instance, Coca-Cola certainly didn’t have the information it needed to make a good decision about New Coke. The company had tested the taste of its new recipe with more than 200,000 consumers, but it never asked people whether they actually wanted a different variety of Coke to replace the old one. Turned out they didn’t.
Polaroid and Kodak definitely lacked clarity on priorities and principles, not to mention alignment throughout the organization. Both companies’ R&D departments had actually developed path-breaking digital cameras. But the divisions that made and marketed film had little interest in encouraging or pursuing nonfilm technology.
To be sure, the decisions that make the history books are huge, bet-the-company choices, the business equivalent of deciding to invade Russia. Most companies face such issues infrequently. But every company makes millions of decisions every year, from big strategic decisions like launching a new product line to week-in, week-out decisions about marketing, procurement or customer service. And even seemingly small decisions can go terribly wrong. In September 2011, for example, Bank of America announced that it would soon begin charging its debit card customers a $5 monthly fee. The move set off a firestorm of consumer protest and the bank was forced to back down.
One thing we can learn from all these cautionary tales is that it’s easy for organizations to foul up their decision processes. When the decision stars fall out of alignment, a company can run into serious trouble quickly.
So it’s worth reflecting on your decisions—the good, the bad and the ugly. Executives can learn much from the pitfalls of the past. They can study up on how to make and execute critical decisions well. On this score, we recommend the new book by Tom Davenport and Brook Manville, Judgment Calls—12 Stories of Big Decisions and the Teams That Got Them Right. And we hope you’ll read our own book, Decide & Deliver: 5 Steps to Breakthrough Performance in Your Organization.
Bad decisions are a bit like Trojan horses—you may not recognize the danger at first, but if you know your history, you’ll soon learn to keep them outside your walls.
Paul Rogers is the managing partner of Bain’s London office and leads Bain’s Global Organization practice. Marcia W. Blenko is a partner with Bain & Company and a senior member of the firm’s Global Organization practice. Jenny Davis-Peccoud is the senior director of Bain’s Global Organization practice. She is based in London.
Growing Interest In AI Technologies
If you want investors to pay attention to your startup, then you have to reassure them beyond a doubt that your technology is going to create the next big bang.Google’s acquisition of the UK based Startup DeepMindTechnologies paved the way for other venture investors to broaden their investment portfolios to include Machine Learning.After all common sense dictates that it is better to take risks and make investment decisions that prove fruitful as opposed to not making any decision at all. But what is Machine Learning and why should you care? The article below by Lorien Pratt explains in 500 words what we need to know about this ground-breaking technology as an investment opportunity and as a solution to some of life’s most wicked problems.
What is machine learning, and why you should care (in 500 words)
October 31, 2015
IntroductionMachine LearningWicked Problems
It’s critical that you understand machine learning, even if just a little bit. Why? Machine learning is at the heart of the most common artificial intelligence systems today. It’s an important new technology that’s moved beyond hype to the brink of an exponential explosion, at the core of a 320% growth in AI-based startups last year. And, in combination with decision intelligence, Machine Learning has the potential to solve some of the most important problems faced by humanity today.
So here’s what you need to know.
Most importantly, machine learning is a way to build computer programs that learn from examples. Instead of writing software, machine learning experts “teach” the computer using tables of data showing what they want it to do. Usually, every row of the table contains two parts, which we could call the “gazintas” and the “gazoutas”. Gazintas might be a list of features of birds: 4″ bill, 10″ legs, brown head, 3″ toes, white eyes, and so forth. Gazoutas might be whether that bird eats fish or not: “yes” or “no” for each row of data.
For example:
GazintasGazoutabill lengthleg lengthhead colortoe lengtheye coloreats fish?43white1.5brownY21green4redN… (and so forth)
Now, here’s the best part:
The miracle of machine learning systems is that they allow computers to program themselves.
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You take this list of examples, give it to a learner, and it produces a learned system, which detects which aspects of a bird are most likely to go along with one that eats fish. It works like this:
So, in our example, the learner might realize that if the legs are over a foot long, and the bill is over 3 inches long, the bird always eats fish. It builds that into the learned system. Then, when you show it a new bird and say “does it eat fish?” it makes a pretty good guess.
That’s the essence of the idea. Everything else is detail and *tweaks on this core concept. Seriously.
Here’s a great video by Sebastian Thrun and Katie Mallone that shows this “core” of machine learning in action.
Why is this amazing?
It allows computers to program themselves, for previously-unsolvable problems like face recognition and book recommendation. This is because the computer can look at millions of examples, and can find subtle patterns that are too hard for the humans.
It’s seriously cool: the core technology at the heart of your favorite AI movies
Machine learning is core to unlimited investment opportunities with huge, proven growth curves,can be built into new systems at your job, and can make you a hero (I’d love to help you with this).
It is at the heart of many tools you use (Netflix, Amazon, Facebook, Google, and more)
When combined with decision intelligence, machine learning can solve the world’s toughest problems
And yes, it can learn to play Super Mario Brothers :-)**
*with apologies to my machine learning friends, who know that the field is much bigger than what I cover here. But this is the most important part
**Image credit:http://micdotcom.tumblr.com/post/121589410557/add-learning-how-to-beat-super-mario-to-the-list
LORIEN PRATT's CASE FOR MACHINE LEARNING
When Lorien Pratt weighs in on a subject we should listen and pay close attention. In her case for Machine Learning as having come of age, she uses some very succinct, mind-blowing analogies that literally stop you in your tracks, telling you to postpone whatever it is you are doing and listen.
She adopts a witty approach that convinces the reader to agree with her that Machine Learning like every other technology deserves its place in Society here and now because Machine Learning is here to meet a specific need. I leave you with these two direct quotes from her article that should whet your appetite and get you read the entire post:
“Today, the explosion of data is creating an adjacent desert: the lack of systems that help you use that data to drive business value, whether it is customer experience, revenue, or cost savings. Machine learning is filling this gap today.”
“I’ve won a lot of new projects this year, with clients that recognize this: data alone is chocolate, not chocolate cake. It is just one ingredient (necessary but not sufficient) to obtain business value from many data sources. My clients have also come to realize that technologies such as machine learning, artificial intelligence, and decision intelligence are needed to take extract the full value from investments in big data.”
Enjoy the article.
Machine learning is poised for mass adoption
October 12, 2015
Artificial IntelligenceData, and its limitsMachine LearningTechnology analysis
Every few years, it’s exciting to witness a nascent technology emerge as a credibly disruptive influence around the world. I was honored to be part of the growth of personal computing in the 1980s, and the growth of the web in the 1990s. Today, machine learning for the masses is on the brink of a similar explosion.
Remember when it was a stretch to think that Granny could understand the difference between software and hardware? These days, she’s got her own computer—inconceivable in 1983!—and uses it for lots of tasks. The same will be true of machine learning, which is the technology that “connects the dots” between data and what she’s interested in. And if she can do it, so can you.
Looking backwards, we’ve already been through the AI/ML hype cycle (remember The Fifth Generation of the 1980s?), and so this second time around, it’s serious. From this point of view, I disagree with where Gartner places Machine Learning on its 2015 emerging technology curve, as shown in the graphic above. We’re not just past the “Peak of Inflated Expectation”: to the contrary, we’re well on the way towards the “Plateau of Productivity”
My evidence comes from (a) my experience living through the Inflated Expectations curve; (b) years living through the Trough and © in 2015, I’m receiving signals that feel much more like mass adoption than disillusionment from inflated expectations, as the curve above would have you believe. Buyers are buying, it’s not just about sellers and marketing.
The hype
Back in the 80s and 1990s I was privileged to contribute machine learning expertise to a number of projects, including the Human Genome Project, DOE hazardous waste analysis, forensic hair analysis with the Colorado Bureau of Investigation, and more.
The trough
Starting in the mid-90s, things cooled down a bit, though, so much that I couldn’t say “Machine Learning” or “Artificial Intelligence” to refer to my work. So for a while, it was all about “data mining”, then “analytics”, then “predictive analytics“. Even as recently as Gartner’s curve last year, “machine learning” was nowhere to be seen:
Entering mass adoption
Today, the explosion of data is creating an adjacent desert: a lack of systems that help you use that data to drive business value, whether it’s customer experience, revenue, or cost savings. This is the gap that machine learning is filling today.
My evidence: I’ve won a lot of new projects this year, with clients that recognize this: data alone is chocolate, not chocolate cake. It’s just one ingredient: necessary but not sufficient to obtain business value from many data sources. My clients have also come to realize that technologies like machine learning, artificial intelligence, and decision intelligence are needed to take extract the full value from investments in big data.
This year, I’ve built machine learning systems for clients in financial modeling, visual pattern analysis, marketing, time series prediction, network optimization, and more. And their expectations of world-class performance are solidly in line with reality.
Driving the machine learning car
Along the way, the discipline of machine learning practitioner is emerging. This is in contrast to the researchers and academics that have dominated the field until now.
It’s like the comparison between a car driver and an auto mechanic. Just as you can drive a car without needing to be able to explain the chemistry inside a carburetor, this separation is emerging in machine learning as well. And expert drivers are just as important as expert mechanics.
I’ve written already about Amazon’s machine learning environment; it’s one of a handful of new players (including Microsoft’s Machine Learning Studio) that’s aimed at an entirely new audience: graduates of community college programs in data science, rather than PhD machine learning developers. But mass adoption will go much further: data science experts represent just one stepping stone along the way.
In 2015, for the first time, advances in Deep Learning, in combination with new user interfaces (in stealth today), the availability of GPU and cluster compute environments, massive data stores, and open data from many sources, means that, this year, we’re well past the hype and have fully entered a world of genuine value from this important new technology.
INFORMATION YOU SHOULD HAVE BEFORE YOU SELECT A CAREER OR BECOME YOUR OWN BOSS.
Moira Forbes shares insightful career advice from 6 women who know what it takes to be your own boss.
I Wish I Knew Then: 6 Power Women Share Most Valuable Career Advice
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You should never be the smartest person in the room. Worry if you are.
Be brave. The price of inaction is far greater than the cost of making a mistake.
What if you had the opportunity to ask today’s most successful leaders to identify the single most important lesson they’ve learned on a given topic? And what if those same leaders were willing to drill down on their personal experiences and share key insights in an honest and relevant way? My guess is that it would be a game-changer for many of us.
“The One Thing” is a new series dedicated to tapping into the wisdom of today’s most dynamic thinkers and ‘doers’. What’s ‘The One Thing’…. about career success, or work-life wellness, or bouncing back from failure…that you absolutely need to know in order to better navigate those types of events in your own life? This series will be a one-stop, go-to guide for women who are looking to embrace the success strategies of proven leaders, across industries and across generations.
Everybody can benefit from a mentor. And while we can’t hit fast forward on personal experience, we can draw on the life lessons of others to enhance our growth or simply help us manage all that we’re looking to accomplish each and every day.
What’s “The One Thing” You Wish You Had Known Starting Out In Your Career?
Know What You Don’t Know
“Surround yourself with people who are smarter than you. You should never be the smartest person in the room. Worry if you are. ”
-Jessica Alba, Founder & Chief Creative Officer, The Honest Co.
Have A Bias For Action
“I would echo what my mother told me, ‘You’ve got to have a bias for action.’ The hockey coaches and the basketball coaches always say, “You will miss a hundred percent of the shots that you never take.” My mother encouraged me to try things that I wasn’t sure I could do. Over my life, I’ve been able to try things, many of which have worked that I didn’t necessarily think they would. Be brave. The price of inaction is far greater than the cost of making a mistake. ”
-Meg Whitman, CEO, HP
Leadership Is All About Adaptability
“Darwin said those who survive are neither the strongest nor the most intelligent, it’s those that can adapt to change. And I wish I had thought about that when I was younger because it always seemed to me that you had to be the brightest or the strongest.There’s something to be said for adapting to change. That doesn’t mean abandoning your values, but it does mean recognizing that the environment has changed and absorbing that .”
-Anne Finucane, Vice Chairman and Global Chief Strategy and Marketing Officer, Bank of America BAC +0.00%
Stay Open To Opportunity
“Be ready. Just be ready. You just don’t know what opportunity might be out there. It may not even be a path that they were thinking of. But other people see your possibilities there. I never intended to run for Congress. I never intended to run for leadership. Other people came to me to encourage me to do so. And I was ready.”
-Nancy Pelosi, House Democratic Leader, The U.S. House of Representatives
Stay True To You
“I would say always, to thyself be true. We’re all born with what we have. Take what you have and do the best you can with it . Know who you are…Feel your way through life. Don’t over-think your way through life, because I think we’re all guilty of that.
-Angela Ahrendts, Senior Vice President of Retail and Online Stores, Apple
It’s All Going To Be Ok
“I would tell myself to relax, that everything works out the way it’s supposed to. If you look back on your life at the things that you stressed out, ‘Oh my gosh, he didn’t ask me out, he didn’t call, I didn’t get that job, I lost that job,’ quite often in the end when one door closes, another one opens. Everything, even though you don’t believe it at the time, works out the way it’s supposed to–the good and the bad.
-Gayle King, Editor-at-Large, O, The Oprah Magazine; Co-Host, CBS This Morning