Author: oskaradmin

Blockchain – promises and pitfalls

Hi there,

With the recent hulabaloo re: Bitcoin, Ethereum etc, I thought I’d spend some time getting myself familiar with these concepts and crypto-currencies.

As some-one who has owned a bit of gold for a long time (seen ups and downs) – due to my inherent distrust in governments being able to control their spending – some of the features of crypto-currencies appeal to me.

The benefits :

  • Cryptocurrencies (eg Bitcoin) can be designed to have a cap / limited amount ever to be issued. This means that they should actually be scarce – you know like the resources on this planet – and through that could be good ‘stores of value’ – basically as a currency should be.
  • As a medium of exchange – with the recent Segwit/Bitcoin cash fork, Bitcoin has a chance to become a good medium of exchange – both for micropayments (e.g via the Lightning network) or currency transfers.
  • Programmable blockchains like Ethereum allow for smart contracts to be implemented on the chain. The ‘ethereum computer’ is actually decentralized network of computers that is ‘Turing complete‘ – meaning that it’s pretty much up to you how complex code you want to write on it.
  • Due to the above programmable nature especially of Ethereum – I think we will see many use cases which will be tried – some will fail, some will  succeed. If you are interested in how blockchain could be applied for example in HR BPO – please contact me here.

The pitfalls :

The main pitfall I would say is still that you have to do your own homework on who / what to trust, but I guess that applies in life generally… The other pitfalls still include a truly easy to use user interface / wallet. However I will investigate those more in detail next.

So yesterday I went to a meetup event called ‘Bitcoin and Cryptocurrency Intro How To Make Money Passively From Home’. The event was hosted in a local Panera Bread, with about 15 people attending. The pitch was for a ‘company’ called BitConnect  where the premise is:

  1. You buy their coin (bitconnect coin or BCC) using Bitcoin(BTC).
  2. The BCC is converted back to USD – and using their HFT (high-frequency trading) algorithms they trade USD vs BTC.
  3. Somehow the Bitconnect team are supposedly able to make daily profits (‘interest’) according the presenter/this chart (no down days):
  4. They state that the high market cap (around 786M USD today, August 18th 2017) is proof of the legitimacy of this platform.

OK, call me a sceptic, here’s why:

  • When I asked about the team behind this, the presenter dodged the question, and no information is available that I can find on the bitconnect.co website. No developers who want to have their names publicly associated with it? Hmm. No early investors? Hmm.
  • From the site “Build trust and reputation in bitcoin and cryptocurrency ecosystem with Open-source platform”. There is no information about what is open source, and this code on Github has one contributor..
  • As Steemit writes – it’s too good to be true, no down days, guaranteed returns, referral schemes etc..
  • You are supposed to send BTC to them – but you are ‘locked in’ for up to 299 days. Guess what will happen to your BTC if they run out of new patsies to pay the old ones..

Bottom-line – there is a ton value that can be created on blockchains – but people – pls do your homework. And if you / or some-one you know is considering bitconnect – caveat emptor…

Cheers,

Oskar

10 Machine Learning use cases for HR

Hi there,

These days we seem to be bombarded daily with articles about Artificial Intelligence, Machine Learning and some version of ‘robots will take your job’… However as of 2016, machine learning is a buzzword, and according to the Gartner hype cycle of 2016, at its peak of inflated expectations.[12] This is because finding patterns is hard, often we don’t have enough training data available, and also because of the high expectations it often fails to deliver – so hold your expectations of having a ‘Jarvis‘-like presence guiding you along – we’re not quite there yet 🙂

 

With that in mind, I thought it would be interesting as an HR/IT professional to try to identify possible use cases in HR for machine learning, and then identify where the most value can be gained from using it. And by HR /Human Resources here I mean all the ‘branches’ of it – including Recruitment, Personnel Administration, Talent & Performance Management, Payroll, Time management, Benefits, Compensation etc etc. I’d be happy to have comments & additions be added to the list, so leave your suggestion/s in the comments section below. With that said, here we go:

Use cases for Machine Learning

1/ Recruitment, candidate attraction – explore your social media data / mentions, and dig down where traffic – with a focus on target market (say front-end engineers) are coming from, in order to achieve more predictable candidate flow. And remember positive impression matter a lot, as Chobani has shown with HR as PR. Then use blog post writing software for example using Narrative Language Generation, in order to improve social media presence.

2/ Recruitment to Talent Management –  explore your data to classify from which industries / areas successful candidates have come. Can you find commonalities between solid performers? You can use for example K-means clustering to find the commonalities between the good performers. However as this article explains, this can lead to bias in your selection (say as most CS graduates are male), so it’s important to back test your algorithm to avoid issues down the road.

3/ Recruitment, candidate selection – use natural language understanding or algorithmic selection tools on candidate resume pools to find suitable candidates.

4/ HR customer service – as explored here, with Natural Language Understanding getting better and better it’s possible to create chatbots (or conversational agents) that help answer employee questions, route questions to human agents, perhaps even do simple updates. Companies in this space include eg. Talla, Kylie,ai. Custom implementations could be done with Slack, Microsoft’s botframework, or IBM Watson conversations etc. I’m working on a ‘Benefits’ chatbot using Watson Conversations and SAP Hana Cloud Platform, welcome to ping me here if you are interested in this space.

5/ New hire on-boarding – chatbots these days use NLP (Natural Language Processing) which ‘understands’ what the employee is intending to communicate. You could create a chatbot or ‘conversational agent’ with new hire guidance, FAQs, role documentation.

6/ Retaining valuable employees – Use salary data, performance, organizational data etc to explore correlations, and ultimately predict which factors predispose to voluntary terminations. You could do these predictions eg. with Ensemble methods / Random Forests, or use classification tools like support vector machines or a K-neighbors classifier to group the employees into ‘buckets’ of ‘high / medium / low’ chance of leaving.

7/ Performance management guidance – provide a chatbot to answer employee queries re: the performance process like this one by SAP/Successfactors.

8/Payroll & Time Management employee questions – provide a chatbot to answer employee queries re: payslips, overtime, absence policies etc, as explored here. The bot also handle repetitive tasks which can be demoralising and unfulfilling (e.g. pre-qualification (what’s this issue about?) or authorisation (how can you prove you are John Smith).

9/ Payroll data connection to talent management – highly structured data found in conventional databases tend to support traditional, highly analytical machine learning approaches. If you have 10+ years of transactional data, then you could use machine learning to find correlations between employee characteristics and pay raises for example.

10/ Career / role recommender system – If you have a large enough organisation with different departments, very varied roles and the data to track where people have previously transferred to or changed roles into, then you could also create a recommender system based on interests, skills, performance, where suitable new roles in your organization could be found. Here’s a comprehensive list of different recommender system – eg. with tools from Amazon, Microsoft Azure or IBM Watson.

Hope this whet your appetite a bit to explore Machine Learning for HR a bit further – and let me any comments or additions below!

Launch festival April 2017

Hi ya,

A few months ago I got a founder ticket to the Launch Festival in San Francisco for April 7th and 8th. After three whirlwind days in the Bay Area here are the things that stuck out to me. Personally for me / Move Correctly my best meeting was actually with Greg, the CEO of Fit3d – since he provided so much actionable, real advice. It’s just fantastic to exchange ideas with some-one in the same industry. Now here are my notes about Launch:

 

Machine Learning

1. The flywheel for Machine Learning – was articulated by Rob May, the CEO of Talla, is the way ML becomes better the more data there is, makes better algorithms, makes a better product, drives more use and more data … So it’s really about who can build the best/ quickest flywheel for your use case / industry, and in that sense (at least the ML companies want to give that impression) this is the time to make major investments in this area, as the first (successful) movers will have a big advantage.

2. Zorroa – are doing really impressive visual recognition from videos – where they can search inside videos / images just as if you were googling documents. So eg locating any scenes where ‘the Rock’ appears, then narrowing it down if it happens in a bank, and further where there is a Lambo in the scene.. Currently you need to plug into their REST API,  but they mentioned a SAAS app in a few months…

3. Corto – their demo of a chatbot analytics interface to a pharmaceutical genomics data was impressive, with hypergraphs / nodes flowing and a lot of complex words in the presentation, so I have no doubt the tech is solid. Their team is chock-full of smart guys, with eg one of the leading AGI guys – Ben Goertzel as Chief Scientist. What wasn’t clear for me is who will sell their product and what is their value prop?

4. The PAC framework by Rob May again, which essentially states that any company should evaluate how they want to use Machine Learning in these categories:

A) P for Predict, eg. in recruitment which candidates will perform best, in sales which products etc.

B) A for Automate, could be easing workflow, say for example NLP (natural language processing) transcribing recruitment interview notes.

C) C for Classify – say classifying best resumes into different buckets quickly

Now apply these questions across your customers, across Product, across Operations, and you should start to identify good opportunities where to apply ML.

5. Talla is a customer service bot either for IT or HR, that’s been trained to answer IT / HR questions, with a UI either in Slack or Microsoft teams. Their target market is in mid-size companies.

6. Kylie.ai –  created a customer service bot, integrated with eg ticketing systems like Zendesk. They’d ‘clone’ employee personalities and create a response integrated into existing UI’s eg on Zendesk, Salesforce, SAP etc, which the human customer service agent can review, modify, approve / send.

Cannabis

The Cannabis market is yuuge apparently as it warranted it’s own vertical, next to healthcare, drones, ML etc. Interesting companies included:

  1. Leaf – built a small growing unit looking like fridge, which automates home growing. Sold about 1M of them in advance and are taking orders for 2018..
  2. Alula Hydro, who have created a hydroponic, nutrient delivery system for industrial growers. The 20K industrial growing management system apparently can raise yields from a crappy 1K per pound to 5K-6K per pound.
  3. Baker, who are making a CRM / loyalty / online store SAAS for dispensaries.

 

Miscellaneous notes

  1.  How to get 1000 applicants for a job ad – by Tucker Max from Book in a box: 
    • Start with the hook – explain the why / the mission of the company, and if some-one doesn’t believe in that clearly they’re not a fit.
    • Sell the role – Talk like you would talk to a friend about the job. Ditch all the standard corporate lingo about ‘mission critical, pro-active go-getter with a nose for global synergies’. 
    • Pleasure and Pain – explain why the company / role is awesome, but clearly also the downsides of the job, no point in trying to sugar-coat stuff.
    • Testimonials / social proof – we use these in any ads (for soda, cars, books etc), why not for job ads?
    • Finally – the actual ‘boring’ details
  2. VR / AR – was actually a bit underwhelming – nothing really that stood out. Yes, it’s cool to get a new, different type of camera or get analytics off VR, but…eh.
  3. In hardware – Megabots was really cool, just in terms of lighting up any 6-year old inside all of us – Robots with fun big weapons (WEABOONS!) and picking up cars 😉

 

5 reasons solar power is coming…

In my view there are several factors, which are adding up in favor of solar electricity. These are now combining to make solar an exponential technology – eg Ray Kurzweil has predicted with current trends, the share of solar doubles every two years, meaning that solar should have a 100% share of the market in 12 years – “it has begun to reliably double its market share every two years — today’s 2% share is up from just 0.5% in 2012.”.  

 

The trend is driven by the following factors:

  1. The cost of solar per Watt DC going down according to the National Renewable Energy Laboratory:
  2. In over 20 US states solar is at grid parity with retail electricity according to Greentech Media.  So why is grid parity important? By grid parity we mean “Residential solar reaches grid parity when the levelized cost of solar energy falls below gross electricity bill savings in the first year of a solar PV system’s life.”  Meaning that you can buy the solar system (even 100% financed, zero down), and you will pay less in the first year, than you would have paid for electricity to your utility company. As the cost of retail electricity will likely continue to rise, and the cost of a solar installation will go lower, GTM estimate 42 states reaching grid parity by 2020.
  3. US Solar PV (photovoltaic) market growth is strong, especially the utility sector is showing a nice hockeystick (probably due to the lower costs).
  4. The fact of the matter is that not many people have an extra 15K-20K laying around, however if you do – you get the best IRR (Internal Rate of Return), around 10% depending on your state, but as high as 30% in MA, and 20% in NY, NJ. These days it’s possible to purchase a solar PV installation using financing, essentially paying for the solar installation using the savings in your electricity bill. I.e. no money down, and after 10 years the system is yours, and the electricity produced from it is ‘free’. The options for lease / purchase for example by sunrun.com shown here;
  5. And the final factor – according to research by Lawrence Berkeley National Laboratory found that home buyers were likely to pay on average $15.000 dollars more for a home with a solar system installed (even small 3.6 KW system), compared to a home without one. So not only would you be able to reduce or eliminate your electricity bill, but you would essentially get the money back on your investment when / if you sell your home.

So, you know this thing about climate change? That it’s not a hoax, even the permafrost in Siberia is thawing etc, right? Given the above info, I know I would install solar power in my house (if having a house was on the radar..), how about you?

Thanks for reading.

Learning Python programming

This is a short post about my experience in learning programming, and Python in particular – how it’s been going, what’s worked and what I’m struggling with. I’ve spent about two hours per day, five days a week, for about 1-2 months now.

As background I’ve worked in IT (consulting, project management, ERP systems, specifically HR&Payroll) for a long time. When I was younger I never really had an inclination to learn programming – I took one course of Java in college, but didn’t like it as the instructor wasn’t very good.

Here are some lessons / nuggets that I’ve found helpful:

  1. Knowing why I’d want learn is motivating for me. I want to be able to hack simpler solutions myself, and so that I can become a better entrepreneur / consultant. Ever since my experiences with Move Correctly , I’ve found that it’s frustrating to have to wait for developers to complete work.  Waiting can be especially taxing if you choose to go with a fixed price project, which by default leaves you less leverage on the project schedule /completion date…. (separate topic..)
  2. I did the Python course on codeacademy – however I felt that many times there was not enough instruction in the course (no videos), so I would bang my head against the wall for sometime an hour / two trying to get some simple function to work.
  3. The Datacamp Intro to Python for DataScience was quite fun, they award you xp based on successful answers/code, however it wasn’t very challenging and I don’t think that I’d learn to write actual code with their approach. They do have other e.g intermediate courses, but to pay 29USD per month – compared to Udemy’s pricing -doesn’t match up.
  4. In December I started taking the “Python for DataScience and Machine Learning bootcamp” and I’m about 40% through it – mostly the data science parts. After going the through the crash-course I’ve learned about Jupyter notebooks and Python libraries such as Numpy, Pandas, Matplotlib, and Seaborn. I like statistics, and it’s cool to be able to extract meaning out of masses of data for sure. However I have a feeling that this post is correct – ‘Data preparation accounts for about 80% of the work of data scientists‘, and I’m not sure that’s for me… Well -this course has the Machine Learning portions coming up, so let’s see. Overall great value, as I picked up the course for 15 USD.
  5. I’m also taking the “Python Mega Course”  and this has been really great, I can highly recommend it, and great value at 10 USD (year end sale). The best portions so far have been:
    1. Learning to write a Windows GUI program (using Tkinter library), with a connection to a SQL database (SQL lite or PostGreSQL).
    2. Learning to write a Python Flask Web app, setting up a Git/Gitbhub profile and deploying the app to Heroku.
    3. Learning the Bokeh library for data visualization on the web, example here. It’s taking a csv file with volcano locations (latitude /longitude) and using the Folium JS library for the viewing in the browser. If you are interested the code looks like this.
  6. Overall I feel I’m now at the stage where I want to start building applications that I’d be interested to see myself – targeting from start of Feb. I’m also at a stage where I can write simple code for myself, but need frequent references to libraries/google/stackoverflow etc.
  7. I will likely also retain a “trainer / programmer” via freelancer.com to help me with the upcoming challenges. A bit like the Thinkful part-time Python bootcamp, but hopefully cheaper :-). I’m planning to try out say 10 sessions/ lessons with another Python programmer to review any questions / issues I’ll have, as well as concepts/tricks etc.

Here are some development projects I’m considering to try out:

  • Simple game using either Kivy or PyGame frameworks
  • Python Django website with user login using social IDs, Paypal/Stripe integration, user data entry etc.
  • An digital health related data visualisation, perhaps with API pulls.

Look for an update on these within one month.. For now if there’s anything you would like me to specifically work on, pls drop me a line here.

10 thoughts on road-schooling

-after a three month experiment with two kids and a wife working

From August to October 2016 we were living in Airbnb’s, while my wife (Jolene) worked and I was road-schooling the kids. I hear you – what the heck is road-schooling? Simple, it’s home-schooling , but it’s done on the road. Now schooling is a somewhat restrictive terms, I’d call it learning while on the road..

Now before you think I’m a nutter that doesn’t believe in ‘structured’ education, let me say this: both Jolene and I have had great experiences in the public schools we’ve gone to from Finland to Singapore to the US. However just because the classroom format of putting 30 kids in one class, having one teacher up front “teaching” – was invented 200 years ago – and it’s worked so far – doesn’t mean that it’s the optimal for all kids.

 

Really, it comes back to what do we believe “education” is for? Is it so that once kids have learned a “high-school equivalent” (whatever that means) amount of “stuff”, which they will prove by writing down this “stuff” on paper, from their own memory – then they will be ready for college? College also seems to be the default aspiration, which is mostly unquestioned.

What are the goals then of education? IMO, as a child has become an adult, the young adult is able to think critically for themselves, they are able to learn new knowledge and new skills by themselves, and they have found a purpose / mission / ulterior motive, which they are pursuing. Now I’m not saying it has to be a ‘save the world’ type thing, but something larger than yourself is probably beneficial..

Now did this trip in particular achieve the above? No. But I think by showing them that there is something larger out there, there is a vast history which we all are connected to, they can better find their own way in the world. And maybe the next trip, when they are a bit bigger…

 

On to the experiment

As I’ve gotten older, and hopefully wiser, I’ve finally taken to heart the advice from my hero – Tim Ferriss – and learned that it’s best if you “try out shit”. See if it works. If it doesn’t  – OK, you learn from it. If it does work – great!

So we did an experiment where we travelled through Europe for three months. Sam was 10 this year, Kate just turned 7.

We visited Finland/Helsinki:

We visited Italy: Rome, Pisa, Florence:

 

We visited France: Paris, Normandy D-day beaches, Mont-saint-Michel, Bayeux tapestry, Loire valley castles:

And we hopped to Barcelona:

 

 

What I would do differently based on this experience:

  1. Kids hate museums. Yeah, you probably could have told me that earlier, but I’m thickheaded, so I guess we had to try… It’s not that they specifically hate everything in museums – but the format is usually boring… Read this tiny script about this ancient spoon which they found in some backwater? Yay. There were a few exceptions to this which the kids liked, more on these later.
  2. We were usually in one place for about one week. The idea was that we’d not have to be switching, travelling too often and give ourselves a more leisurely pace to explore. Well – it was still too harried. We had booked all the flights in advance, but e.g finding new Airbnb accommodation for each week – turned out to be a chore. With hind-sight, I’d probably spend two weeks in the more interesting locations (eg. Florence for me).

What worked in terms of learning, enjoyment: 

3. Unstructured play – there were several times when the kids were playing with say a swing, laughing, not a care in the world, no time frame set. No program to rush to, no lessons to attend. I don’t know about you, but I think that’s what being a kid is about.

4. Caen World War II memorial with the time-line of the WWII was really instructive and Sam now has a solid grasp of it, while he enjoyed it at the same time.

5. Visit farms, zoos – many times we’d visit a zoo or an animal farm, and since we’d be in no hurry the kids could feed the animals, hang out with them, while learning about the animals and the environment ‘by osmosis’ almost. Especially Kate seemed to enjoy this.

6. One of us not working... (me :).. It would have been a pain to have to school the kids, work and travel. Now Jolene was able to hold the fort financially, while me and the kids did the chores – including making home-cooked meals, shopping for groceries etc. For example in Italy even the ready-made dinners tasted great!

7. Traveling together with family: we had one week together in the Loire valley with my mom. This worked out great since we all got to experience a new place together, my mom is a franco-phile, so she could guide us, speak to the locals etc.

8. We did physical education everyday. We’d go out to local parks to run, sprint, swing, play soccer, climbing, jumping etc, and at home do hand-stands, planks, push-ups, wheelbarrows, rows etc. It just takes a little bit of imagination, but it’s totally worth it. The kids would be sweaty and work up an appetite, while it helped counter-act all the desserts/gelato/cappucino we ‘had to’ try everyday…

9. Khan Academy & IXL

Both of these online resources were fabulous. For math both Sam Kate were able to breeze through their grade level on Khan Academy (k to 2 for Kate, 5th grade for Sam) math in less than three months. Now I’m not assigning any special status to the kids due to this – I just think that a good online system and one-on-one support are probably much more efficient than the standard class-room format.

10. Structured days

IMO it worked best to have a clear structure to everyday, eg along these lines:

Lesson 1 – 9AM to 9.50 eg. Chinese/Math/Swedish

Lesson 2 – 10AM to 10.50 Physical education (preferably outdoors)

Lesson 3 -11AM to 12PM – Science or Math

12PM-1PM  fix lunch, have the kids help out. Eat together as a family.

1PM- 2PM Relax/ read / prep afternoon / kids play on iPads

2PM— > Go out to visit sights, tourismo..

As a final tip, if you can combine your trip/road-schooling with visiting family, that’s a bonus as well. This year we got to spend time in Helsinki with my family, in Mississippi with Jolene’s brother and we’ve now moved to Gainesville where Jolene’s sister, mom and dad live as well. Especially for those of us who have lived away from family these are precious moments.

Thank you for reading this far – hope you enjoyed it 🙂

 

Chatbots for HR and payroll

Hi there,

This blog page is an experiment with creating a chatbot -or ‘conversational agent’ which can help an HR/Payroll organisation customer service agents to handle the routine, easier questions.

Service center agents often have to handle repetitive tasks which can be demoralising and unfulfilling (e.g. pre-qualification (what’s this issue about?) or authorisation (how can you prove you are John Smith)). The chatbot can do that instead.

Customers don’t like being put on hold or wait in a phone queue – the chatbot is always there and ready to answer questions. Finally customers don’t like to repeat themselves- since all the conversations are logged in the thread, the information is available also to a human customer service rep if required.

I’ve built this below CA (conversational agent) to be able handle simple queries like:

  • “can you send me my payslip”?
  • “What was my overtime balance for the previous month”?
  • “Connect with support team”
  • etc…

There is an “authorisation step” when you are requesting either a payslip or overtime, accepted name values are: “John”, “Sally” and “Pentti”.

These are just some simple examples, more can easily be configured. Give it a go here:

As you’ll note there is no back-end integration at the moment, however the conversational agent is able to greet the customer, understand the queries, do an authorisation check and direct her further if needed.

Pls let me know what you think in the comments, or contact me via the contact page.

Thanks

Oskar