9/16/2018

Current State of Machine Learning and Artificial Intelligence

Machine Learning has three main features.  

For some, recommendation engines one websites are valuable tools, perhaps suggesting good movies similar to ones you've watched already, based on preferences

For some, clustering of homes on real estate sites are good tools.  What is my home value based on similar homes within the region.

And for others, classifying images is a good tool, perhaps recognizing your friends in a picture on social media, without having to enter the tags in manually.

Recommendation, Clustering and Classifying.  Three solid pieces of machine learning or types.  And a variety of algorithms to get to that goal.

There's also Neural Networks, that are useful for artificial intelligence, they have weights that get toggled true or false, depending on incoming variables, with multiple layers, which produce an end result weighted probability.

Some common themes are lots of data, to train the model, to learn from known data.  some data needs to be labeled ahead of time, which is "Supervised Learning".  The opposite is having the model learn on its own, with the luxury of "tagged" data ahead of time, called "Unsupervised Learning".

There're also models that learn from other models, to reinforce learned behavior, the event happened, this was the result, let me track that, for future reference.

Some Neural Nets are very large, with hundreds of layers deep, to get fairly precise results.  They do require more compute power and memory and take time to process.

There are a slew of languages and tools to use when working with machine learning and artificial intelligence, both on-premise, virtual machines and in the Cloud.

The fact we have bigger data sets, better compute power, more ram and machines chips that can crunch more data faster.  In the past, typically large institutions had access to these types of machines, so Universities or large computer organizations were the only place to work on this technology.  Now you can program this on a laptop in you living room if you desire.  So things have tricked down into the hands of the many.  Thus, faster progress, cleaner solutions, better results.

The results have gotten better, in image recognition, speech recognition, translation tools from language to language, some in real time and many more.  At this point machine learning is a useful tool to assist humans in everyday activities.  It has not gotten to the point where AI is replacing everyday jobs, in most cases.  But the tides will shift at some point, where people are competing with smart machines for everyday jobs.  And when robots are tasked with specific tasks, that too will automate some of the workforce.  And when smart machines can perform complex tasks in real time with minimal error, then we will take notice as computers do not necessarily require vacation time, or health insurance, or 401k matching contributions, and they work 24/7 without complaining or forming Unions.

So, we have come a long way, computers can crunch data and translate into meaningful information for consumption, to lower costs and automate menial tasks.  Where do we go from here?  More automation, easier to use tools, better integration across domains, access in everyday tasks, embedded IoT devices that assist in real time.  Artificial Intelligence should not be interpreted strictly as computers rising up, taking over man kind.  Surely, anything is possible, at this point in time, we are still telling the machines what to do.  And they do their specific tasks, in a sort of black box, and we do not necessarily know exactly how the results were derived.

With Moore's Law, things will only get faster, cheaper, easier to use and proliferate through everyday society.  That should keep us busy for a while, for sure.



9/10/2018

Put the Hat in the Ring

I went down pretty hard.  The bags of food balanced on the handlebars went everywhere.  The bike took a spill.  Injured wrist & shoulder.

Perhaps self sabotage.  Was flying out the next day.  To play a tennis tournament.  In Long Island.  National Indoor Clay, Under 35's.  I decided to go anyway.

Although won a lot of matches locally, national stage is different.  Most of these players knew each other.  As they played each other for years.  At the college level.  Pros.

I arrived at the club early.  Hit with a guy.  Decided to enter the doubles draw.  My opponent was driving in from DC, was late.  Tournament director let the match be delayed for hours.  Got a chance to watch some good matches.

My opponent arrived, went to the courts, indoor clay.  I got waxed.  Didn't get a single game.  My doubles partner probably thought about backing out.  As we had to play the number one seed.  individually both ranked in top 10, doubles, top 5.

Warmed up, each person won their serve.  The ball moved fast, quick points.  I served with full force, regardless of the pain.  Went to a tie breaker first set.  They squeaked it out.  Went on to win the 2nd set.

For me, still a victory.  To be on the court with top players in the country, gave a good fight.  No regrets.

We got a doubles ranking for that match, nationally.

Later that year, the National Outdoor Clay tournament in Daytona, FL, drove for this one.

Played a different guy from DC, I won that match.  Next match, one of the top seeds, same guy we played doubles in NY earlier that year.  Played a tough match, yet it wasn't enough.  He said after the match, he didn't remember the 2 handed backhand first time around, "your two hand backhand is a weapon".

Dropped into the consolation bracket.  Won the the next match.  Then played a guy from Boston.  We had similar game, get everything back, run down every ball, both had strong backhands.  I went up early, won the first set, he won the second.  It was real hot that day, took a break between sets.  By that time, lots of spectators watching, clapping, lots of long points.  After 2.5 hours, we shook hands, he went on to the next round.

I said, "I thought I was in pretty good shape, but you seemed to be in better shape."  He replied, "I told my coach I didn't want to lose due to fitness."  It worked.

Finished out the year with Florida ranking, National ranking both singles and doubles.  Also taught lessons around town, fill in for other teaching pros, group lessons, kids, moms, adults, private lessons.

After meeting my wife, decided to go back to work doing Crystal Reports consultant job, day after I accepted offer, got another offer to teach tennis full time at one of the clubs.  Had to decline.  And set down the rackets, again.

That was a good ride though.  Rose the ranks locally, singles and doubles leagues, local tournaments, and finally national tournaments and stint as teaching pro.  Teaching was a great opportunity and I enjoyed time on the courts.

That was 14 years ago.  Would be tough to find time to get back into tennis at that level.  Then again, you never know.

And so it goes~!

9/03/2018

Hop Aboard the Data Bandwagon

It does appear the Cloud is the place to be, certainly gained traction past few years.  Few key players to choose from.  Offer mostly the same services, just depends on what flavor you prefer, how well it integrates into current stack, and your developer pool staff skills and availability.

It seems Machine Learning is still hot along with Artificial Intelligence, although it bypassed many folks from the data pool skills set, requiring new languages like Python, R, Scala, Spark, Notebooks and algorithms.

Big Data didn't solve all our problems, but it's a nice addition to the data stack, has some good hooks to get data in and out, as well as reporting, although it won't be our traditional transaction database as expected.

Organizations still want their data, in readable format, in timely manor, with accuracy expected.  Although hiccups in the Extract Transform and Load still seem to play havoc on our daily reporting needs.

Data Science still seems hot, although just because you have talented DS, doesn't mean they can write standard SQL or knock out some traditional reports.  The two seem mutual exclusive and don't necessarily overlap, as new college grads jump straight into DS with little to no knowledge of traditional data ecosystem.

Data Engineer is a hot position, joining disparate data sets for others to work with.

Data Management has probably risen the fastest, as new GDPR rules necessitate solid data practices.  With that, having knowledge of where all the data resides, what it contains, how to access has pushed the Metadata into the spotlight, with Data Catalogs to handle such requests.

We are no longer at the point where knowing SQL or Access or Excel can guarantee you a data position.  Data skills have proliferated, grown, splintered, gone sideways and every which way.  The Data Ecosystem has exploded and many new comers have entered the arena, as developers, architects, software vendors and applications.

Throw in Blockchain, Agile Methodology, Streaming, IoT and Domain Knowledge, you can clearly have your hands full for the next decade or so.

Suffice to say, data is hot.  Anyone entering the workforce that's looking for a solid career, should look no further than data.  It's the bread and butter of every department in every organization.  There are some good companies foaming at the mouth, to get some talent in the door, to hit the ground running, and add value across the board.

So hop aboard the Data Bandwagon.