3: AI for Everyone – Building an AI company – Notes


Case Study: Smart Speaker

  • “Hey Device, tell me a joke”
    • Steps (AI Pipeline):
      1. Trigger work/wakeword detection A) Hey device”? -> B) 0/1
      2. Speech recognition A) Audio -> B) “tell me a joke”
      3. Intent recognition A) Joke? vs, B) time?, music?, call?, weather?
      4. Execute joke
    • These could be 4 different teams
  • “Hey device, set timer for 10 minutes”
    • Steps (AI Pipeline):
      1. Trigger work/wakeword detection A) Hey device”? -> B) 0/1
      2. Speech recognition A) Audio -> B) “Set timer for 10 minutes”
      3. Intent recognition A) “set timer for 10 minutes -> B) Timer
      4. Execute
        1. Extract duration 
          1. “Set timer for 10 minutes”
          2. “Let me know when 10 minutes is up”
        2. Start Timer with set duration
  • Challenge:
    • Each function is a specialized piece of software.
    • This requires companies to train users on what the speaker can, and can not do.

Case study: Self driving car

  •     Steps for deciding how to drive
    1. Image/Radar/Lidar
      • Car detection
      • Pedestrian detection
    2. Motion planning 
      • Steer/acceleration/Brake
  • Key Steps
    1. Car detection (supervised learning)
    2. Pedestrian detection (supervised learning)
    3. Motion Planning (SLAM – Simultaneous localization and mapping)
  • Challenge:
    • Each function is a specialized piece of software.

Roles in AI teams

  • Software Engineers (30% +)
  • Machine Learning Engineer. focused on A -> B mapping
  • Applied ML Scientist: Using State of the art to today’s problems
  • Machine Learning Researcher. Extend the state-of-the-art in ML
  • Data Scientist. Examine data and provide insights. Make presentation to team/executives. Some may be Machine Learning Engineer.
  • Data Engineers. Organize data. Make sure data is saved in an easily accessible, secure, and in a cost effective way
  • AI Product Manager. Help define what to build. What feasible and valuable

AI Transformation Playbook

  1. Execute pilot projects to gain momentum
    • Success is more important than value
      • Need to get the flywheel moving
    • Show traction within 6 to 12 months (quiz said 6 to 10)
    • Can be in-house or outsourced
  2. Build an in-house AI team
    • Can be under: CTO, CIO, CDO, or CAIO
    • Have a central AI center of excellence. Matrix them in to start, untill understanding of AI is throughout the org
    • CEO should provide funding to start, not from BU.
  3. Provide broad AI training
  4. Develop an AI strategy
    • You do this at step 4 to gain concrete experience, vs starting with an academic strategic approach to something so new.
    • Leverage AI to create and advantage specific to your industry sector
    • Design strategy aligned with the “Virtuous Cycle of AI”
      • Better product -> More users -> More data -> [Repeat]
    • Create a data strategy
      • Strategic data acquisition
      • Unified data warehouse/lake
    • Create network effects and platform advantages
      • In industries with “winner take all/most” dynamics, AI can be an accelerator
    • Leverage classic frameworks as well. Low cost/ focus
    • Consider humanity.
  5. Develop internal and external communication
    • AI can change a company and its products
    • Investor relations. to properly value your company
    • Government relations. to align on regulations.
    • Consumer/use education
    • Talent/recruitment
    • Internal communications. to address questions and concerns.

AI pitfalls to avoid

  • Don’t
    • Expect it to do everything
    • Hire 2-3 ML engineers and expect then to come up with use cases.
    • expect it to work the first time\
    • Don’t expect Traditional planning process to apply yo AI
    • Don’t wait for a superstar, get going with what you have today.
  • Do
    • Be realistic
    • Beginners should be linked with business
    • Work with  the AI team to develop new timelines, KPIs, etc

Taking your first steps

  • Get friends to learn about AI
    • Courses
    • Reading group
  • Start brainstorming projects (no project too small)
  • Hire a few ML/DS people to help
  • Hire or appoint an AI leader
  • Discuss with CEO/Board possibilities of AI transformation
    • Will the company be more valuable, or more effective if we are good at AI.

Supervised learning

Unsupervised learning

Transfer learning


Knowledge graphs

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Quick Take: The Great Decoupling of Retail

There is a great decoupling happening in retail, a structural change. Similar to the decoupling that the computing industry went through, going from being vertically integrated to horizontal specialist. What does this mean for retailers? Retailers need to be clear on what their unique selling proposition is, that is why do customers choose them vs their competitor, or substitute; then double down on those things. Is it your wide assortment, price, convenience, customer service, maybe safety now, or some thing less rational. Everything else should be considered for outsourcing to horizontal specialist, those who are optimized to delivery a particular service, or product.

Within any company, typically the most valuable resources are centered around the making and the selling organizations. There is no difference in retail; instead it’s the merchandising and store operations organizations, and I would add human resources to being core. Most other functions should be evaluated for their need to be an in-house capability.

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2: AI for Everyone – Building AI Projects – Notes


  • Starting an AI project

    • Workflow of projects

    • Selecting AI projects

    • Organizing data and team for the projects

Workflow of a machine learning project

How do you build, say a speech recognition engine

  • Key Steps:

    1. Collect Data: people saying “Alexa”, and other words

    2. Train model: learns A to B mapping… audio clip to “word”

      • many iterations

    3. Deploy the model: implement in to a smart speaker

      • Will collect new data (get data back), to  maintain /update the model

How do you build, say a self driving car

  • Key steps:

    1. Collect Data: images – > positions of other cars, draw rectangles around cars

    2. Train model: need to iterate and precisely identify cars

    3. Deploy model: may learn that golf carts are identified and positions well. keep iterating.

Workflow of a data science project

output: actionable insights

Optimize a sales funnel

  • Key steps:

    1. Collect Data: where are people coming from, time of day, machines type, etsc…

    2. Analyze the data: Iterate many time to get good insights insights from the data collected.

    3. Suggest hypotheses/actions: Deploy changes, re-analyze new data periodically.

Optimizing a manufacturing line

  • Key steps:

    1. Collect Data: clay supplier, mixing time, ingredients, lead times, relative humidity, temperature, kiln duration, etc…

    2. Analyze the data: Iterate many time to get good insights insights from the data collected. 

    3. Suggest hypotheses/actions: Deploy changes, re-analyze new data periodically.

Every job function needs to learn how to use data

  • Use data to optimize workflows through data science based analysis, and to take on tasks with machine learning (remember less than a second), Inputs (A) to Output (B).

  • From Sales, recruiting, marketing, to agriculture, and beyond DS and ML are having huge impacts

How to choose an AI project

  • Bring together a cross-functional team knowledgeable in AI, plus domain experts.

  • Brainstorming framework:

    • Think about automating “tasks,” vs automating “jobs.”

    • what are the main drivers of business values?

    • What are the main pain point in your business?

    • Note: you can make progress without big data

      • Having more data almost never hurts

      • Data makes some business [Google, Facebook, Netflix, Amazon] defensible.

      • With small datasets, you can still make progress. The amount of data you need is problem dependant.

  • Due diligence on project

    • What AI can do + Valuable for your business

      • Technical diligence

        • Can AI system meet desired performance (e.g. accuracy, speed, etc)

        • How much data is need to meet performance goals

        • Engineering timline

      • Business diligence

        • Current business: Lower costs

        • Current business: Increase revenue ( getting more people to check out)

        • New business: New product or business

      • *Ethical diligence*

        • money vs impact on society

  • Build vs. buy

    • ML projects can be in-house or outsourced

    • DS projects are more commonly in-house

    • Some things will be industry standard, avoid building those.

  • “Don’t sprint in front of a train.”

    • some times it makes sense to adopt another’s platform or approach than to build your own. resource constraints, capability constraints…

Working with an AI team

  • Specify your acceptance criteria

    • Goal: defects with 95% accuracy…How do you measure accuracy

      • Test Set (n1000): labelled training dataset to measure performance. 

    • Training Set: Pictures with labels

      • Learn mapping from A to B

    • Test Set: Another data set to test the mappings. Often more than 1 test set will be requested.

  • Pitfall of expecting 100% accuracy. Discuss with AI engineers what’s reasonable.

    • Limitations of ML

    • Insufficient data

    • Mislabeled data

    • Ambiguous labels

Technical Tools for AI teams

  • CPU vs. GPU [Great for deep Learning/Neural Networks] Nvidia

  • Cloud vs. On-prem, ….Edge [Processor, where data is collected.]

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1: AI for Everyone – Introduction to Artificial Intelligence – Notes

What is AI?

What is Machine Learning?

  • Supervise Learning… Input (A) to output (B) learning

What is Data?

  • Need to determine what is A (Input – Features) and what is B (Output) on the inputs
  • Acquiring data via manual labeling, observation, available downloads
  • Once you start collecting data, start feeding it to a AI team for a feedback loop to start. Start this early. 
  • You can not assume value in data, because you have a lot of it. Becareful, have an AI with to asses
  • Garbage in garbage out exists here too

The Terminilogy of AI

  • AI(ML, et al (DL/NN, et al))…Data Science cuts across all of these.

What makes an AI company?

What machine learning can and cannot do

  • Technical diligence: looking at the data, look at the input, and output A and B, and just thinking through if this is something AI can really do.
    • Can NOT do market research and write an extended market report
  • Complex interactions require lots of examples. the system would either remain vague in response, or return incoherent responses.
  • What makes an ML problem easier
    1. Learning s “simple” concept <= 1 second of human thought…Cause we have to formulate? why?
    2. Lots of data available
  • AI has a hard time of inferring intention of action in context A= hand gesture, B= stop, hitchhiker, left turn on a bike, hello, etc…

Deep Learning

  • Deep Learning and Neural Network are used interchangeably
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SAS Network Design Cheat Sheet

Transportation Problem

Step 1: Write proc optmodel;

proc optmodel;

That was easy.

Step 2: Create all your “assets”.

I call them assets, but you can call them whatever you want. Think of them as anything in the problem that we can assign attributes to. In this case, we have the Plants and the Regions. So we will create two sets, one for each. That’s why we use the command “set”.

set Plant={“Plant 1″,”Plant 2”};

set Region={“Region 1″,”Region 2”, “Region 3”};

I like calling things by their name, so I can remember them. Make sure to use {} for the sets, and to put each name inside quotes.

Step 3: Input all your data

This is all the information that in being shared in the problem. They will generally be attributes for each of the assets, or they can be constants. For all of these we use the command number:

number demand{Region} = [25 95 80];

number capacity{Plant} = [100 125];

number cost{Plant, Region} = [

250 325 445

275 260 460


The general nomenclature is that you give a name to the attribute, put what asset it describes inside {} and then put the values inside []. When writing tables in, make sure to write {rows,columns}. In the case of the cost, the plants would be the rows, and the regions would be the columns. Make sure that the order of the numbers you input matches the order of the items you defined in your set; SAS will assign the first number to the first item in the set, and so on. It also helps to space out tables to better read them and catch mistakes.

Step 4: Establish your variables

In this case, variables are the flows from the Plants to the Regions.

var flow{Plant, Region} >= 0;

Putting Plant,Region inside the {} means we will have a flow for each combination of Plant and Region. This means six variables will be created flow(Plant 1, Region 1), flow(Plant 1, Region 2), flow(Plant 1, Region 3), flow (Plant 2, Region 1), flow(Plant 2, Region 2), and flow(Plant 2, Region 3). Always remember to define what type of variable it is. In this case, we want our variables to be non-negative.

Step 5: Define your objective function

I called it z here, but you can call it anything you want, like TotalCost if you want to be more descriptive

minimize z = sum{i in Plant, j in Region}flow[i,j]*cost[i,j];

When you write sum {i in Plant, j in Region }, it’s the equivalent of doing a sumproduct in Excel. SAS will take every combination of Plant and Region and do the described calculation of flow times cost, and then add them all up. So in this case, it would take flow[Plant 1, Region 1]*cost[Plant 1, Region 1], then flow[Plant 1, Region 2]*cost[Plant 1, Region 2], do that for all the combinations, and then compute the sum.

I could’ve used p instead of i, and r intead of j, like sum {p in Plant, r in Region}flow[p,r]*cost[p,r]), or any other letter for that matter as long as I remain consistent and I haven’t used that letter to describe something else already.

Step 6: Establish your Constraints

In this case there are only two general contraints: capacity contraints for the plants, and demand constraints for the regions:

con capacitycon{i in Plant}: sum{j in Region}flow[i,j] <= capacity[i];

con demandcon{j in Region}: sum{i in Plant}flow[i,j] >= demand[j];

Let’s take as an example the first constraint. On the left side of the :, con capacitycon{i in Plant}, establishes the name of the constraint as capacitycon. The {I in Plant} means this is a constraint that applies individually to every Plant, so the constraint will be calculated individually for every item in the set Plant. This means this constraint really works as two constraints in this case, one using values for Plant 1, and another using values for Plant 2.

After the “:” we define the constraint itself. Here, SAS will take the flow and change the value j using every  region, and add them up, and it will compare that to the capacity of the Plant. Since we established before the “:” that the constraint would be calculated separately for each Plant, the Plant remains constant for each iteration of the constraint calculation. In essence, it would take the flow(P1, R1) + flow(P1, R2) + flow(P1,R3) and make sure that is less that or equal to the capacity of (P1). Again notice that the Plant doesn’t change; instead, the constraint will do the calculations for Plant 2 as a separate comparison, because we established the {i in Plant} on the left of the “:”.

Step 7: Wrap it up

solve; print z flow; expand; quit;

Don’t forget to check the log for any error (the middle tab between you code and the results). SAS will highlight any mistakes you would’ve made there. Just scroll up to see where the first red line of text is, and that was your first mistake. You can follow these steps any time you are using SAS with this notation.

Transhipment Problem

Network Facility Location Problem

    “transcost” was not used in the sas files, as it is not required when the transportation cost is $1. If it is any other value, will need to add that in.

Network Facility Location Problem w/ LOS

    copy and paste matrix from spreadsheet…

to calculate the binary table dynamically, see below from page  12


For the demand constraint “demandcon“, use an equality instead of inequality as otherwise the solution will create non-integer demand, just to meet the LOS constraint. 

Advanced Supply Chain Network Design

    Supply Chain Network Design Problem

Multi-Commidity Flow Problem:

    Data tables come before “proc optmodel;”


 INFILE Statement Options

DELIMITER= option—Specifies what character (other than the blank default character) to         use as the delimiter in files that are being read. Common delimiters include comma (,), vertical pipe (|), semi-colon (;) , and the tab. For example, to specify a vertical pipe as the delimiter, the syntax is DLM=’|’, as shown here: infile ‘C:\mydata\test.dat’ dsd dlm=’|’ lrecl=1024;

A tab is specified by its hexadecimal value. For ASCII systems (UNIX, Windows, and Linux), the value is ’09’x. For EBCDIC systems (z/OS and MVS), the value is ‘05’x. As an example, the syntax to specify a tab delimiter on an ASCII system is DLM=’09’x. Note: The positioning of the quotation marks and the x in hexadecimal values is critical. No space is allowed between the x and the quotation marks, as shown in this example: infile ‘C:\mydata\test.txt’ dsd dlm=’09’x truncover;

In my case I have troubles with the ASCII so I used dlm=’,’; and I separated the data with , and it run.    

Reading Delimited Text Files.pdf

/* Inputing values of multi-dimensional matrix incost in a table form first*/

Data incost;

infile datalines dsd delimiter=’09’x;

input Product $ Plant $ DC $ incost;


P1 Chicago Atlanta 6

P1 Chicago Boston 5

P1 Dallas Atlanta 4

P1 Dallas Boston 7

P1 Miami Atlanta 6

P1 Miami Boston 9

P2 Chicago Atlanta 6

P2 Chicago Boston 5

P2 Dallas Atlanta 4

P2 Dallas Boston 7

P2 Miami Atlanta 6

P2 Miami Boston 9

P3 Chicago Atlanta 6

P3 Chicago Boston 5

P3 Dallas Atlanta 4

P3 Dallas Boston 7

P3 Miami Atlanta 4

P3 Miami Boston 7



/* Inputing values of multi-dimensional matrix outcost in a table form first*/

Data outcost;

infile datalines dsd delimiter=’09’x;

input Product $ DC $ Region $ outcost;


P1 Atlanta NY 8

P1 Atlanta VA 5

P1 Atlanta PA 6

P1 Boston NY 9

P1 Boston VA 7

P1 Boston PA 6

P2 Atlanta NY 7

P2 Atlanta VA 8

P2 Atlanta PA 5

P2 Boston NY 3

P2 Boston VA 8

P2 Boston PA 6

P3 Atlanta NY 7

P3 Atlanta VA 4

P3 Atlanta PA 4

P3 Boston NY 4

P3 Boston VA 5

P3 Boston PA 4



Fixed Planning Horizon Problem

Aggregate Planning And Distribution Channel Strategies

Aggregate Planning (including numerous factors like hiring and firing, production levels, etc)

Aggregate Planning with Demand Elasticity (including factors like hiring and firing, production levels, discounts, etc)

Omni-channel Network Design

Reverse Logistics…for Batteries

Optimization Based Procurement

Simple Auctions

Capacity by Lane Constraint

Level of Service Constraint

Supplier Capacity Constraint

Minimum $$ Volume Constraint

Combinatorial Bids

Combinatorial Bids (Min 2 carriers) – Add the following constraint to the previous model.

SAS Files



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Three Quick Thoughts: Amazon Go 3000 #OPINION

  1. Mechanical Turk at Work: Given the complexity involved in training “AI’s” to identify people, identify products, and associate and disassociate the two, I believe that humans are in the loop. This integration of humans and AI is referred to as collective intelligence, and I believe it’s behind the assuredness of the system…and that is O.K. This would be a smart use of humans to accelerate the deployment of the platform, which allows for more data to be collected. This type of system requires a TON of data to build a corpus that would allow it to make high confidence predictions autonomously. I’m sure that system has learned a lot since launching to just employees; it’s still a baby. MTurk crowd can review video leveling judgement calls, in low confidence situations, and continue to label data to drive increased “understanding” for the system. Again, this would be smart, creating more labeled training data, never hurts. With Chicago being added to Seattle…Mo’ data, Mo’ data! Everyone always gets caught up on the number of cameras, and “…look, there are still employees.” It’s truly about the data, not the current quantity of infrastructure and staff. As more data is collected….less and less of both will be needed; on an asymptote of a curve.

  2. Ready for Americas Next Killer Franchise: I don’t think Amazon is going to stray from what has worked with extending their capabilities as a service. We’ve now seen it with AWS infrastructure service, FBA, Amazon Delivery Service Partner Program, and other marketplace services. However, I believe the business model will be franchise based, helping that entrepreneur own their own business – “Amazon Go Store Partner Program.” That is how I think the “just walk out” technology will become a profit center,  and not just a capital cost of doing business for Amazon.

  3. Real-Estate Discovery, Check: Amazon has considered the implementation of physical stores for a very long time. I would bet a majority of the real-estate discovery work was completed as a part of the Amazon Book Store planning process. This pre-work, will allow them to move more quickly with targeting specific locations for prospective franchisees.


  • I predict that “Just Walk Out” technology will be in a Whole Foods within 18 months. Instead of aligning with the naysayers who say it can’t be done in a bigger box store, Amazon will do it!
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What is your customer data share?

By the year 2020, it is estimated that the average person will generate 1.5 GB worth of data per day. At that point, it won’t be just about your firm’s share of customer wallet; instead, you be asked… what’s your firm’s customer data share? Oh, and consider today who owns the majority of insight in to “your” customer.

[Infographic Credit: Domo]

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#010: This Game is Not One-Dimensional, You Have to Face the Customer with Shawn Harris, Global Innovation Strategy Lead, Zebra Technologies.

In this episode of The IoClothes Podcast, we speak with Shawn Harris, Global Innovation Strategy Lead for Zebra Technologies. The reality is, innovative products don’t just sell themselves and companies aren’t composed of just designers, developers and engineers. Someone has to interface with the customer, and keep the ship sailing along a strategic path, which includes profitability (that’s if you want to stay in business). Today, we shift gears and talk a bit about the struggles of retail, the importance of differentiating yourself in the marketplace and how are current relationship with MS Excel may be a sign of the future!

For more information on Zebra Technologies, check out their website at www.zebra.com, on Twitter @ZebraTechnology and on Facebook @ZebraTechnologiesGlobal. You can also follow Shawn on Twitter @SmarterRetailer

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AI and the Final Judgement.

I believe that for the foreseeable future, human machine collaboration, which will lead to a new type of collective intelligence will be a requirement. Today, we are seeing many advances in the ability of machines to make high confidence predictions; in narrow use cases. This will certainly lead to significant shifts in tasks within job roles. However, where I think machines will lack for some time is in judgement. Judgement being the ability to consider multiple predictions, and come to sensible conclusions in context. This is where humans will need to remain in the loop, playing a key role in judgement. This human machine partnership will lead to a collective intelligence that will results in even higher confidence outcomes, than either could realize individually. However, this prescribed union could quickly find the machine taking more and more of the judgement role as well. Just consider Google’s search results; at this point, how many of us ever get to the second page of results. What’s happening here is that we are trusting Google algorithm’s judgement. We could debate the consequences of this for search results; as we endeavor to use AI in medicine, autonomous cars, and other decisioning based on potentially biased data, we will need to take a more deliberate approach to the policies and practices around final judgement.

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Startups at the Intersection of AI and Blockchain

  • Decentralized Intelligence: TraneAI (training AI in a decentralized way); Neureal (peer-to-peer AI supercomputing); SingularityNET (AI marketplace); Neuromation (synthetic datasets generation and algorithm training platform); AI Blockchain (multi-application intelligence); BurstIQ (healthcare data marketplace); AtMatrix (decentralized bots); OpenMined project (data marketplace to train machine learning locally);
  • Conversational Platform: Green Running (home energy virtual assistant); Talla (chatbot); doc.ai (quantified biology and healthcare insights);
  • Prediction Platform: Augur (collective intelligence); Sharpe Capital (crowd-source sentiment predictions);
  • Intellectual Property: Loci.io (IP discovery and mining);
  • Data provenance: KapeIQ (fraud detection on healthcare entities); Data Quarka (facts checking); Priops (data compliance); Signzy (KYC)
  • Trading: Euklid (bitcoin investments); EthVentures (investments on digital tokens). For other (theoretical) applications in finance, see Lipton (2017);
  • Insurance: Mutual.life (P2P insurance), Inari (general);
  • Miscellaneous: Social Coin (citizens’ reward systems); HealthyTail (pet analytics); Crowdz (e-commerce); DeepSee (media platform); ChainMind (cybersecurity).

Read the full article: The convergence of AI and Blockchain: what’s the deal? by Francesco Corea

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Lifelong Learning

I believe that lifelong learning is essential to career success. Technology is changing every industry, and each segment within. Today, you cannot rest on your undergraduate and graduate degrees, and on the job experiences alone. You have to go back to school. But, you don’t have to go back to a school. MOOCS, or Massively Open On-line Courses have transformed learning. Now, you can advance your knowledge, with courses from top universities; from the comfort of your couch. Platforms like edx.org, coursera.com, getsmarter, and numerous others allow you to take many courses for free (audit), or for a relatively small fee earn a certificate of completion, which proves you’ve successful completed a given program. I am a huge proponent of these platforms. For the past year, I have been doing the micromasters in Supply Chain Management via MIT CTL & Edx, and soon will be starting a course in Artificial Intelligence via MIT CSAIL & Getsmarter. No question, it’s a lot of work, when you work and have a family, but it’s worth it. Stay hungry, stay foolish.

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The Technology Behind Alibaba

Highlights from a recent article in the South China Morning Post:

“The widespread application of cutting-edge artificial intelligence and machine learning by the e-commerce juggernaut Alibaba Group is borne out of necessity – the sheer volume of products that would be moved would make it practically impossible for employees to keep up, and is part of a broader push by China to embrace AI…Jack Ma, executive chairman of Alibaba Group,said in October that the company’s front end garners most of the attention even though it was strong in technology. “People tend to recognise our platforms and services, but overlook the technologies that make it happen””

Key Points:

  • “China has set a target to build a 1 trillion yuan (US$150 billion) AI industry by 2030…”
  • “…Smart Selection, an AI-powered recommendation algorithm will, in no small measure, help buyers to make a decision…compared with seasoned fashion industry professionals, big data and AI would excel in picking products such as clothes where there are numerous brands and variables if evaluated manually.”
  • “The recommendation algorithm is backed by the latest advances in deep learning and natural language processing, which is also used by Amazon in its recommendation engine and text-to-speech service Polly.”
  • “AI-powered customer service chatbot Dian Xiaomi is another of Alibaba’s tech tools to help make businesses smarter and more efficient…the chatbot can understand more than 90 per cent of customer enquiries and serve almost 3.5 million users a day.”
  • “…the more advanced “cloud” version [of the AI-powered customer service chatbot]… features capability to understand customers’ emotion through text analysis…”
  • “About 200 robots – automated guided vehicles – will work round the clock to deliver the orders placed at a newly opened automated warehouse operated by Alibaba’s delivery arm, Cainiao Network, in Huizhou…”
  • ““These 200 robots can process 1 million shipments per day,”…“They are three times more efficient than manual operations and need to be charged for just one hour after every six hours of use.”…“All the robots are automatically connected with each other and they assign shipping tasks themselves without a central control room.””
  • “The efficiency of the logistics service means customers could receive same-day deliveries – orders placed in the morning would be delivered to customers’ doorsteps in the afternoon.”
  • “…it was difficult to find people to work in warehouses the robots could easily cover the labour shortage. “We will have more such smart warehouses next year.””
  • “…JD.com, China’s second largest e-commerce giant, also has its own smart warehouse where only robots operate. Besides, the Alibaba rival launched JD-X in May, a logistics lab to develop robots, drones and smart warehouses.”
  • “JD.com, which offers drone delivery services in the western city of Xian and eastern China’s Suqian, said in August it would offer 100 million yuan to the winners of a competition to find the best solution for conducting widespread drone delivery services across China.”
  • “…Alibaba used drones to deliver packages over open water for the first time. The flying robots delivered six boxes…weighing a combined 12 kilograms…Alibaba said it would consider using the drones in the future to deliver high value products…”
  • “Alibaba will spend more than US$15 billion to open seven research labs in Beijing, Hangzhou, Singapore, Moscow, Tel Aviv, San Mateo, and Bellevue as part of the project. These will focus on areas that include machine learning, network security, visual computing and natural language processing.”

Read the full article: Alibaba lets AI, robots and drones do the heavy lifting on Singles’ Day

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Alibaba Found Lots of Love on Singles Day 11.11.17

The Chinese’s Guanggun Jie, or Singles Day, takes place each year on 11/11. A day set aside to celebrate being proud to be single, has become one of the largest consumer shopping days of the year, dominated by one ecommerce giant Alibaba. Last year (2016), Alibaba processed $17.8B in Gross Merchadise Volume (GMV), which doesn’t represent Alibaba’s corporate revenue, but instead the total value of the goods sold on the platform. Alibaba mostly earns revenue on advertising placements on the platform. Yesterday, for Singles Day 2017, a whopping $25.3B was purchased through the Alibaba platform. This represented a 39% YoY increase. Wow. However, what this also continues to demonstrate is the structural shift underway in retail, where one digital platform can attract and process more volume in a day, than most retailers do in a year. Think about it, today we’re talking about Alibaba, not the 140,000 brands and retailers who provided the products. Who owns the customer, really? That’s huge!

Below is an infographics published by Alibaba:

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In conclusion…

  • Retailers you can’t run from this. You can’t regard these times as being temporary in nature waiting for market improvements; this change is not cyclical, it’s structural. Focus on your value in the chain…
    • Coopetition, work with other retailers, including classic competitors.
    • Sell the operations, franchise…
    • Partner with new world distribution, let them aggregate … Facebook, Google, etc; you fulfill.
    • Become a fashion brand, move up the stack via well priced, on-trend private label.
  • I’m a big believer in focusing in on how to think vs. what to think. If you chase the what in these times, you’re reacting too late. If you know how to think, you have the opportunity to be a defining/benchmark setting retailer. Never forget, focus on your customer, not the product.
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Did Amazon just patent tech that could end showrooming in its stores?

“I would be shocked if Amazon implemented this tech as described. I do think they would implement the tech to monitor in-store web traffic to gain insights to make the overall shopping experience better. I would recommend other retailers do the same; many are still struggling to make sense of the data they have.” ~ Shawn Harris

Read the Full Article: http://www.retailwire.com/discussion/did-amazon-just-patent-tech-that-could-end-showrooming-in-its-stores/

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Who Am I?

“I am who I am today, because of yesterday. I look forward to seeing who I will be tomorrow, because of today.” ~ Shawn Harris

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“Jeff, what does Day 2 look like?”

“It’s all about culture, culture, culture.” ~Shawn

Jeff Bezo’s 2016 Letter to Shareholders

April 12, 2017

“Jeff, what does Day 2 look like?”

That’s a question I just got at our most recent all-hands meeting. I’ve been reminding people that it’s Day 1 for a couple of decades. I work in an Amazon building named Day 1, and when I moved buildings, I took the name with me. I spend time thinking about this topic.

“Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always Day 1.”

To be sure, this kind of decline would happen in extreme slow motion. An established company might harvest Day 2 for decades, but the final result would still come.

I’m interested in the question, how do you fend off Day 2? What are the techniques and tactics? How do you keep the vitality of Day 1, even inside a large organization?

Such a question can’t have a simple answer. There will be many elements, multiple paths, and many traps. I don’t know the whole answer, but I may know bits of it. Here’s a starter pack of essentials for Day 1 defense: customer obsession, a skeptical view of proxies, the eager adoption of external trends, and high-velocity decision making.

True Customer Obsession

There are many ways to center a business. You can be competitor focused, you can be product focused, you can be technology focused, you can be business model focused, and there are more. But in my view, obsessive customer focus is by far the most protective of Day 1 vitality.

Why? There are many advantages to a customer-centric approach, but here’s the big one: customers are always beautifully, wonderfully dissatisfied, even when they report being happy and business is great. Even when they don’t yet know it, customers want something better, and your desire to delight customers will drive you to invent on their behalf. No customer ever asked Amazon to create the Prime membership program, but it sure turns out they wanted it, and I could give you many such examples.

Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight. A customer-obsessed culture best creates the conditions where all of that can happen.

Resist Proxies

As companies get larger and more complex, there’s a tendency to manage to proxies. This comes in many shapes and sizes, and it’s dangerous, subtle, and very Day 2.

A common example is process as proxy. Good process serves you so you can serve customers. But if you’re not watchful, the process can become the thing. This can happen very easily in large organizations. The process becomes the proxy for the result you want. You stop looking at outcomes and just make sure you’re doing the process right. Gulp. It’s not that rare to hear a junior leader defend a bad outcome with something like, “Well, we followed the process.” A more experienced leader will use it as an opportunity to investigate and improve the process. The process is not the thing. It’s always worth asking, do we own the process or does the process own us? In a Day 2 company, you might find it’s the second.

Another example: market research and customer surveys can become proxies for customers – something that’s especially dangerous when you’re inventing and designing products. “Fifty-five percent of beta testers report being satisfied with this feature. That is up from 47% in the first survey.” That’s hard to interpret and could unintentionally mislead.

Good inventors and designers deeply understand their customer. They spend tremendous energy developing that intuition. They study and understand many anecdotes rather than only the averages you’ll find on surveys. They live with the design.

I’m not against beta testing or surveys. But you, the product or service owner, must understand the customer, have a vision, and love the offering. Then, beta testing and research can help you find your blind spots. A remarkable customer experience starts with heart, intuition, curiosity, play, guts, taste. You won’t find any of it in a survey.

Embrace External Trends

The outside world can push you into Day 2 if you won’t or can’t embrace powerful trends quickly. If you fight them, you’re probably fighting the future. Embrace them and you have a tailwind.

These big trends are not that hard to spot (they get talked and written about a lot), but they can be strangely hard for large organizations to embrace. We’re in the middle of an obvious one right now: machine learning and artificial intelligence.

Over the past decades computers have broadly automated tasks that programmers could describe with clear rules and algorithms. Modern machine learning techniques now allow us to do the same for tasks where describing the precise rules is much harder.

At Amazon, we’ve been engaged in the practical application of machine learning for many years now. Some of this work is highly visible: our autonomous Prime Air delivery drones; the Amazon Go convenience store that uses machine vision to eliminate checkout lines; and Alexa, our cloud-based AI assistant. (We still struggle to keep Echo in stock, despite our best efforts. A high-quality problem, but a problem. We’re working on it.)

But much of what we do with machine learning happens beneath the surface. Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type – quietly but meaningfully improving core operations.

Inside AWS, we’re excited to lower the costs and barriers to machine learning and AI so organizations of all sizes can take advantage of these advanced techniques.

Using our pre-packaged versions of popular deep learning frameworks running on P2 compute instances (optimized for this workload), customers are already developing powerful systems ranging everywhere from early disease detection to increasing crop yields. And we’ve also made Amazon’s higher level services available in a convenient form. Amazon Lex (what’s inside Alexa), Amazon Polly, and Amazon Rekognition remove the heavy lifting from natural language understanding, speech generation, and image analysis. They can be accessed with simple API calls – no machine learning expertise required. Watch this space. Much more to come.

High-Velocity Decision Making

Day 2 companies make high-quality decisions, but they make high-quality decisions slowly. To keep the energy and dynamism of Day 1, you have to somehow make high-quality, high-velocity decisions. Easy for start-ups and very challenging for large organizations. The senior team at Amazon is determined to keep our decision-making velocity high. Speed matters in business – plus a high-velocity decision making environment is more fun too. We don’t know all the answers, but here are some thoughts.

First, never use a one-size-fits-all decision-making process. Many decisions are reversible, two-way doors. Those decisions can use a light-weight process. For those, so what if you’re wrong? I wrote about this in more detail in last year’s letter.

Second, most decisions should probably be made with somewhere around 70% of the information you wish you had. If you wait for 90%, in most cases, you’re probably being slow. Plus, either way, you need to be good at quickly recognizing and correcting bad decisions. If you’re good at course correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive for sure.

Third, use the phrase “disagree and commit.” This phrase will save a lot of time. If you have conviction on a particular direction even though there’s no consensus, it’s helpful to say, “Look, I know we disagree on this but will you gamble with me on it? Disagree and commit?” By the time you’re at this point, no one can know the answer for sure, and you’ll probably get a quick yes.

This isn’t one way. If you’re the boss, you should do this too. I disagree and commit all the time. We recently greenlit a particular Amazon Studios original. I told the team my view: debatable whether it would be interesting enough, complicated to produce, the business terms aren’t that good, and we have lots of other opportunities. They had a completely different opinion and wanted to go ahead. I wrote back right away with “I disagree and commit and hope it becomes the most watched thing we’ve ever made.” Consider how much slower this decision cycle would have been if the team had actually had to convince me rather than simply get my commitment.

Note what this example is not: it’s not me thinking to myself “well, these guys are wrong and missing the point, but this isn’t worth me chasing.” It’s a genuine disagreement of opinion, a candid expression of my view, a chance for the team to weigh my view, and a quick, sincere commitment to go their way. And given that this team has already brought home 11 Emmys, 6 Golden Globes, and 3 Oscars, I’m just glad they let me in the room at all!

Fourth, recognize true misalignment issues early and escalate them immediately. Sometimes teams have different objectives and fundamentally different views. They are not aligned. No amount of discussion, no number of meetings will resolve that deep misalignment. Without escalation, the default dispute resolution mechanism for this scenario is exhaustion. Whoever has more stamina carries the decision.

I’ve seen many examples of sincere misalignment at Amazon over the years. When we decided to invite third party sellers to compete directly against us on our own product detail pages – that was a big one. Many smart, well-intentioned Amazonians were simply not at all aligned with the direction. The big decision set up hundreds of smaller decisions, many of which needed to be escalated to the senior team.

“You’ve worn me down” is an awful decision-making process. It’s slow and de-energizing. Go for quick escalation instead – it’s better.

So, have you settled only for decision quality, or are you mindful of decision velocity too? Are the world’s trends tailwinds for you? Are you falling prey to proxies, or do they serve you? And most important of all, are you delighting customers? We can have the scope and capabilities of a large company and the spirit and heart of a small one. But we have to choose it.

A huge thank you to each and every customer for allowing us to serve you, to our shareowners for your support, and to Amazonians everywhere for your hard work, your ingenuity, and your passion.

As always, I attach a copy of our original 1997 letter. It remains Day 1.



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Will a new TJX concept put more hurt on department stores?

“The key to TJX’s success is their merchants. They are constantly on the hunt for high-value, on-trend, opportunistic buys. This creates the treasure hunt, and a compelling reason to shop … frequently. I think TJX will launch a full assortment off-price furniture chain, instead of it just being a department in HomeGoods. It’s not department stores that should be worried, it’s full-priced traditional furniture stores who should keep their eyes wide open.” ~Shawn Harris

Read the Full Article: http://www.retailwire.com/discussion/will-a-new-tjx-concept-put-more-hurt-on-department-stores/

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