Tony Pearson is a Master Inventor and Senior IT Architect for the IBM Storage product line at the
IBM Executive Briefing Center in Tucson Arizona, and featured contributor
to IBM's developerWorks. In 2016, Tony celebrates his 30th year anniversary with IBM Storage. He is
author of the Inside System Storage series of books. This blog is for the open exchange of ideas relating to storage and storage networking hardware, software and services.
(Short URL for this blog: ibm.co/Pearson )
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IBM is in a transition from being a "Systems, Software and Services" company, to become the leading "Cognitive Solutions and Cloud Platform" company. IBM has been in this transformation for the past three years or so, and [over 40 percent of its revenue] now comes from these strategic initiatives.
The purpose of AI and cognitive systems developed and applied by the IBM company is to augment human intelligence. Our technology, products, services and policies will be designed to enhance and extend human capability, expertise and potential. Our position is based not only on principle but also on science.
Cognitive systems will not realistically attain consciousness or independent agency. Rather, they will increasingly be embedded in the processes, systems, products and services by which business and society function -- all of which will and should remain within human control.
For cognitive systems to fulfill their world-changing potential, it is vital that people have confidence in their recommendations, judgments and uses. Therefore, the IBM company will make clear:
When and for what purposes AI is being applied in the cognitive solutions we develop and deploy.
The major sources of data and expertise that inform the insights of cognitive solutions, as well as the methods used to train those systems and solutions.
The principle that clients own their own business models and intellectual property and that they can use AI and cognitive systems to enhance the advantages they have built, often through years of experience. We will work with our clients to protect their data and insights, and will encourage our clients, partners and industry colleagues to adopt similar practices.
The economic and societal benefits of this new era will not be realized if the human side of the equation is not supported. This is uniquely important with cognitive technology, which augments human intelligence and expertise and works collaboratively with humans.
Therefore, the IBM company will work to help students, workers and citizens acquire the skills and knowledge to engage safely, securely and effectively in a relationship with cognitive systems, and to perform the new kinds of work and jobs that will emerge in a cognitive economy.
This week, I was reminded that back in 2011, Watson beat two human players, Ken Jennings and Brad Rutter on the TV game show "Jeopardy!" On his last response, Ken wrote "I for one welcome our new computer overlords." With IBM investing heavily in Cognitive Solutions, should people be worried, or welcome the new technology?
Back in 1950, Isaac Asimov proposed "Three laws of robots":
A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey orders given it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
Let's take a look at how Artificial Intelligence has been represented in the movies over the past few decades. I have put these in chronological order when they were initially released in the United States.
(FCC Disclosure and Spoiler Alert: I work for IBM. This blog post can be considered a "paid celebrity endorsement" for cognitive solutions made by IBM. While IBM may have been involved or featured in some of these movies, I have no financial interest in them. I have seen them all and highly recommend them. I am hoping that you have all seen these, or at least familiar enough with their plot lines that I am not spoiling them for you.)
2001: A Space Odyssey
Back in 1968, Stanley Kubrick and Arthur C. Clarke made a masterpiece movie about a mysterious obelisk floating near Jupiter. To investigate, a crew of human beings takes a space ship managed by a sentient computer named [HAL-9000].
(Many people thought HAL was a subtle reference to IBM. Stanley Kubrick clarifies:
"By the way, just to show you how interpretation can sometimes be bewildering: A cryptographer went to see the film, and he said, 'Oh. I get it. Each letter of HAL's name is one letter ahead of IBM. The H is one letter in front of I, the A is one letter in front of B, and the L is one letter in front of M.'
Now this is a pure coincidence, because HAL's name is an acronym of heuristic and algorithmic, the two methods of computer programming...an almost inconceivable coincidence. It would have taken a cryptographer to have noticed that."
Source: The Making of 2001: A Space Odyssey, Eye Magazine Interview, Modern Library, pp. 249)
The problem arises when HAL-9000 refuses commands from the astronauts. The astronauts are not in control, HAL-9000 was given separate orders from ground control back on earth, and it has determined it would be more successful without the crew.
In 1973, Michael Crichton wrote and directed this movie about an amusement park with three uniquely themed areas: Medieval World, Roman World, and Westworld. Robots are used to staff the parks to make them more realistic, interacting with the guests in character appropriate for each time period.
A malfunction spreads like a computer virus among the robots, causing them to harm or kill the park's guests. Yul Brenner played a robot called simply "the Gunslinger". Equipped with fast reflexes and infrared vision, the Gunslinger proves especially deadly!
(Michael Crichton also wrote "Jurassic Park", which had a similar story line involving dinosaurs with catastrophic results!)
Last year, HBO launched a TV series called "Westworld", based on the same themes covered in this movie. The first season of 10 episodes just finished, and the next season is scheduled for 2018.
Directed by Ridley Scott, this 1982 movie stars Harrison Ford as Rick Deckard, a law enforcement officer. Rick is tasked to hunt down and "retire" four cognitive androids named "replicants" that have killed some humans and are now in search of their creator, a man named J. F. Sebastian.
(I enjoy the euphemisms used in these movies. Terms like kill, murder or assassinate apply to humans but not machines. The word "retire" in this movie refers to destruction of the robots. As we say in IBM, "retirement is not something you do, it is something done to you!")
Destroying machines does not carry the same emotional toll as killing humans, but this movie explores that empathy. A sequel called "Blade Runner 2049" will be released later this year.
In 1983, Matthew Broderick plays David, a young high school student who hacks into the U.S. Military's War Operation Plan Response (WOPR) computer. The WOPR was designed to run various strategic games, including war game simulations, learning as it goes. David decides to initiate the game "Global Thermonuclear War", and the military responds as if the threats were real.
Can the computer learn that the only way to win a war is not to wage it in the first place? And if a computer can learn this, can our human leaders learn this too?
In this series of movies, a franchise spanning from 1984 to 2009, the US Military builds a defense grid computer called [Skynet]. After cognitive learning at an alarming rate, Skynet becomes self-aware, and decides to launch missiles, starting a nuclear war that kills over 3 billion people.
Arnold Schwarzenegger plays the Terminator model T-800, a cognitive solution in human form designed by Skynet to finish the job and kill the remainder of humanity.
In this 2004 movie, Will Smith plays Del Spooner, a technophobic cop who investigates a crime committed by a cognitive robot.
(Many people associate the title with author Isaac Asimov. A short story called "I, Robot" written by Earl and Otto Binder was published in the January 1939 issue of 'Amazing Stories', well before the unrelated and more well-known book 'I, Robot' (1950), a collection of short stories, by Asimov.
Asimov admitted to being heavily influenced by the Binder short story. The title of Asimov's collection was changed to "I, Robot" by the publisher, against Asimov's wishes. Source: IMDB)
Del Spooner uncovers a bigger threat to humanity, not just a single malfunctioning robot, but rather the Virtual Interactive Kinesthetic Interface, or simply VIKI for short, a cognitive solution that controls all robots. VIKI interprets Asimov's three laws in a manner not originally intended.
In this 2015 movie, Domhnall Gleeson plays Caleb, a 26 year old programmer at the world's largest internet company. Caleb wins a competition to spend a week at a private mountain retreat. However, when Caleb arrives he discovers that he must interact with Ava, the world's first true artificial intelligence, a beautiful robot played by Alicia Vikander.
(The title derives from the Latin phrase "Deus Ex-Machina," meaning "a god from the Machine," a phrase that originated in Greek tragedies. Sources: IMDB)
Nathan, the reclusive CEO of this company, relishes this opportunity to have Caleb participate in this experiment, explaining how Artificial Intelligence (AI) will transform the world.
(The three main characters all have appropriate biblical names. Ava is a form of Eve, the first woman; Nathan was a prophet in the court of David; and Caleb was a spy sent by Moses to evaluate the Promised Land. Source: IMDB)
The premise is based in part on the famous [Turing Test], developed by Alan Turing. This is designed to test a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
Movies that depict the bad guys as a particular nationality, ethnicity or religion may be offensive to some movie audiences. Instead, having dinosaurs, monsters, aliens or robots provides a villain that all people can fear equally. This helps movie makers reach a more global audience!
Of course, if robots, androids and other forms of Artificial Intelligence did exactly what humans expect them to, we would not have the tense, thrilling action movies to watch on the big screen.
This is not a complete list of movies. Enter in the comments below your favorite movie that features Artificial Intelligence and why it is your favorite!
(As IBM is focused on its transformation from a "Systems, Software and Services" company to a "Cognitive Solutions and Cloud Platform" company, it seems appropriate to highlight my 1,000 blog post on the concept of cognitive solutions.)
A lot of people ask me to explain what exactly does IBM mean by "cognitive", which is a fair question. Let's start with the [Dictionary definition]:
of or relating to cognition; concerned with the act or process of knowing, perceiving, etc.
of or relating to the mental processes of perception, memory, judgment, and reasoning, as contrasted with emotional and volitional processes.
What exactly does IBM mean by Cognitive? IBM has taken this definition, and focused on four key strategic areas:
In the summer of 1981, I spent a summer debugging a "Pascal" compiler at the University of Texas at Austin. I wasn't told that was what I was doing. Rather, I was tasked with writing sample Pascal programs that would demonstrate the features and capabilities of the language.
Every day, I would come up with a concept of a program, punch up the cards, run it through the CDC hopper, and verify that it would work properly. If I didn't have it working by lunch, I would take it to the "help desk", they would look it over, and tell me how to fix it after I got back.
Most of the time, it was a mistake in my software. A few times, however, it was a flaw in the compiler itself. My programs were basically test cases, and the Pascal Compiler development team was fixing or enhancing the compiler code every time I had a problem.
Compilers basically work by parsing the program text, looking for fixed keywords that are entered in a specifically prescribed order to make sense. Other keywords may represent data types, variables, constants or pre-defined macros.
But compilers are not cognitive. Cognitive solutions can understand natural language, and have to handle all the ambiguity of words not being in the correct order, or different words having different meanings.
As an Electrical Engineer, I had to take many classes on classical analog signal processing. In fact, all computers have some amount of analog components, where threshold processing is used to differentiate a zero (0) from a one (1).
For example, if a "zero" value was represented by 1 volt, and a "one" value by 5 volts, then you can set a threshold at 3 volts. Any voltage less than 3 would be considered a "zero" value, and anything 3 volts or greater a "one" value.
But threshold processing is not cognitive. Cognitive solutions also use thresholds, but their thresholds are dynamically determined, through advanced analytics and statistical mathematical models, and may adjust up and down as needed, based on machine learning over time.
IBM Research is proud to have developed the world's most advanced caching algorithms for its storage systems. Cache memory is very fast, but also very expensive, so offered in limited quantities. Caching algorithms decide which blocks of data should remain in cache, and which should be kicked out.
Ideally, a block in read cache would be kicked out precisely after the last time it was read, with little or no expectation for being read again anytime soon. Likewise, a block in write cache would be destaged to persistent storage precisely after the last time it was updated, with little or no expectation for being updated again anytime soon.
Traditional approach is "Least Recently Used" or [LRU]. Cache entries that were read recently or updated recently, would be placed on the top of the list, and the least referenced would be at the bottom of the list. When space is needed in cache, the entries at the bottom of the list would be kicked out.
IBM's [Adaptive Cache Algorithm outperforms LRU]. For example, on a workstation disk drive workload, at 16MB cache, LRU delivers a hit ratio of 4.24 percent while ARC achieves a hit ratio of 23.82 percent, and, for a SPC1 benchmark, at 4GB cache, LRU delivers a hit ratio of 9.19 percent while ARC achieves a hit ratio of 20 percent.
But caching algorithms, including IBM's Adaptive Cache, are not cognitive. These algorithms respond pragmatically based on the current state of the cache. Cognitive solutions learn, and improve with usage. This is often referred to as "Machine Learning".
The human-computer interface (HCI) has much room for improvement in a variety of areas.
Take for example a snack vending machine. In college, we had assignments to simulate the computing logic of these. We had to interact with the buyer, receive coins entered into the slot--nickels, dimes and quarters representing 5, 10 and 25 cents--determine a total monetary balance, and then dispense snacks of various prices and return an appropriate amount of change, if any. There is even a [greedy algorithm] designed to optimize how the change is returned.
But vending machines are not cognitive. Like the caching algorithms, vending machines interact based on fixed programmatic logic, treating all buyers in the same manner. Cognitive solutions can interact with different users in different ways, customized to their needs, and these interactions can improve over time, based on machine learning.
IBM is exploring the use of Cognitive Solutions in a variety of different industries, from Healthcare to Retail, Financial Services to Manufacturing, and more.
Well, it's Tuesday again, and you know what that means? IBM Announcements!
(Yes, OK, it's actually Thursday. I wrote this post weeks ago, but was embargoed until Jan 10, and then was asked to wait until Jan 12 so that the IBM Marketing team could translate my text into 15 different languages.)
This week, the IBM DS8000 team announces a new High Performance Flash Enclosure (HPFE-Gen2) and a series of All-Flash Array DS8880F models that exploit this new technology.
New High Performance Flash Enclosure (HPFE-Gen2)
The original HPFE was 1U high with 16 or 30 flash cards, and could support RAID-5 or RAID-10. Most used RAID-5, resulting in four array sites of 6+P each, leaving two cards for spare. These 1.8-inch cards were only 400 or 800 GB in size, so the maximum raw capacity was only 24TB per 1U enclosure.
The new HPFE-Gen2 enclosure is a complete re-design, consisting of two Microbays and two TeraPacks. The I/O Bays attach to the Microbays via PCIe Gen3. The Microbays in turn attach to both TeraPacks via redundant 6 Gb or 12 Gb SAS.
Each TeraPack holds 24 flash cards each. Since the TeraPacks come in pairs, you can install 16, 32 or 48 flash cards per enclosure. Each 16-card set represents two array sites, for a maximum of six array sites per HPFE-Gen2.
RAID-5 for 400/800 GB. Two 6+P arrays, four 7+P arrays, and two spares.
RAID-6 for 400/800/1600/3200 GB. Two 5+P+Q arrays, four 6+P+Q arrays, and two spares.
RAID-10 for 400/800/1600/3200 GB. Two 3+3 arrays, four 4+4 arrays, and four spares.
(Technically, these new "Flash cards" are 2.5-inch Solid State Drives (SSD) placed into the HPFE Gen2 connected to the PCIe Gen3 interface, with 50 percent additional capacity to tolerate up to 10 drive-writes-per-day (DWDP). IBM will continue to call them "Flash Cards" for naming consistency between the two generations of HPFE.)
The new HPFE-Gen2 enclosures are substantially faster, offering up to 90 percent more IOPS, and up to 268 percent more throughput (GB/sec). The Microbays use a new flash-optimized ASIC to perform the RAID calculations.
New All-Flash Array DS8880F models
IBM introduces the DS8884F, DS8886F and DS8888F that are based entirely on the HPFE-Gen2 enclosures described above.
Hybrid - HDD/SSD/HPFE mix
Hybrid - HDD/SSD/HPFE mix
AFA - HPFE only
AFA - HPFE-Gen2 only
AFA - HPFE-Gen2 only
AFA - HPFE-Gen2 only
New zHyperLink connection
Also, as a "Statement of Direction", IBM intends to deliver field upgradable support for zHyperLink on existing IBM System Storage DS8880 machines for connection to z System servers. zHyperLink is a short-distance, mainframe-attach link designed for lower latency than High Performance FICON.
Typical latency with FICON/zHPF is around 140-170 microseconds, and this new zHyperLink is estimated to reduce this down to 20-30 microseconds, but is limited to 150 meter fiber optic cable distance. zHyperLink is intended to speed up DB2® for z/OS® transaction processing and improve active log throughput.
Last month, I had the pleasure to help train Watson in its latest mission, to help answer questions from sellers, this are not just for the IBM feet on the street, but also for IBM distributors and IBM Business Partners as well.
"... [survey by SearchYourCloud] revealed 'workers took up to 8 searches to find the right document and information.' Here are a few other statistics that help tell the tale of information overload and wasted time spent searching for correct information -- either external or internal:
'According to a McKinsey report, employees spend 1.8 hours every day -- 9.3 hours per week, on average -- searching and gathering information. Put another way, businesses hire 5 employees but only 4 show up to work; the fifth is off searching for answers, but not contributing any value.' Source: [Time Searching for Information]
'19.8 percent of business time -- the equivalent of one day per working week -- is wasted by employees searching for information to do their job effectively,' according to Interact. Source: [A Fifth of Business Time is Wasted]
IDC data shows that 'the knowledge worker spends about 2.5 hours per day, or roughly 30 percent of the workday, searching for information ... 60 percent [of company executives] felt that time constraints and lack of understanding of how to find information were preventing their employees from finding the information they needed.' Source: [Information: The Lifeblood of the Enterprise]."
In the early days of the Internet, before search engines like Google or Bing, I competed in [Internet Scavenger Hunts]. A dozen or more contestants would be in a room, and would be given a list of 20 questions to find answers for. Each of us would then hunt down answers on the Internet. The person to find the most documented answers before time runs out wins. It was quite the challenge!
Over the years, I have honed my skills as a [Search Ninja]. With over 30 years of experience in IBM Storage, many sellers come to me for answers. Sometimes sellers are just too lazy to look for the answers themselves, too busy trying to meet client deadlines, or too green to know where to look.
A good portion of my 60-hour week is spent helping sellers find the answers they are looking for. Sometimes I dig into the [SSIC], product data sheets, or various IBM Redbooks.
Other times, I would confer with experts, engineers and architects in particular development teams. Often, I learn something new myself. In a few cases, I have turned some questions into ideas for blog posts!
It was no surprise when I was asked to help train Watson for the new "Systems SmartSeller" tool. This will be a tool that runs on smartphones or desktops to help answer questions that sellers might need to respond to RFP or other client queries.
The premise was simple. Treat Watson as a student at "Cognitive University" taking classes from dozens of IBM professors, in a series of semesters, or "phases".
Phase I involved building the "Corpus", the set of documents related to z Systems, POWER systems, Storage and SDI solutions; and a "Grading Tool" that would be used as the Graphical User Interface. I was not involved in phase I.
Phase II was where I came in. Hundreds of questions are categorized by product area. I worked on 500 questions for storage. For each question, Watson had up to eleven different responses, typically a paragraph from the Corpus. My job as a professor was to grade the responses to some 500 storage questions:
★ (one star)
Irrelevant, answer not even storage-related
★★ (two stars)
Relevant, at least it is storage-related, but does not answer the question, or answers it poorly
★★★ (three stars)
Relevant, adequately answers the question
★★★★ (four stars)
Relevant, answers the question well
Most of the answers were either 1-star (not storage related) or 2-star (mentioned storage, but poor response). I would search through the existing Corpus looking for a better answer, and at best found only 3-star responses, which I would add to the list and grade as a 3-star response.
I then searched the Internet for better answers. Once I found a good match, I would type up a 4-star response, add it to the list, and point it to the appropriate resources on the Web.
Other professors, who were also looking at these questions, would then get to grade my suggested responses as well. Watson would learn based on the consensus of how appropriate and accurate each response was graded.
I don't know where the Cognitive University team got some of the questions, but they were quite representative of the ones I get every week. In some cases, the seller didn't understand the question he heard from the client, making it difficult for me to figure out what they were actually asking for.
It reminds me of that parlor game ["Telephone" or "Chinese Whispers"], in which one person whispers a message to the ear of the next person through a line of people until the last player announces the message to the entire group. I have actually played this at an IBM event in China!
Watson needs to parse the question into nouns and verbs, and use that Natural Linguistic Programming (NLP) to then search the Corpus for appropriate answer. I determined three challenges for Watson in this case:
The questions are not always fully formed sentences. For example, "Object storage?" Is this asking what is object storage in general, or rather what does IBM offer in this area?
The questions often do not spell the names of products correctly, or use informal abbreviations. "Can Store-wise V7 do RtC?" is a typical example, short for "Can the IBM Storwize V7000 storage controller perform Real-time Compression?"
The questions ask what is planned in the future. "When will IBM offer feature x in product y?" I am sorry, but Watson is not [Zoltar, the fortune teller]!
I managed to grade the responses in the two weeks we were given. Part of my frustration was the grading tool itself was a bit buggy, and I spent some time trying to track down some of its flaws.
The next phase is in late January and February. This will give the Cognitive University team a chance to update the Corpus, improve the grading interface, and find more professors and different set of questions. I volunteered the most recent four years' worth of my blog posts to be added to the Corpus.
Maybe this tool will help me turn my 60-hour week back to the 40-hour week it should be!