22 Matching Annotations
  1. Jun 2020
    1. One journalist quoted in the paper said: "I spend all my time reading about how automation and AI is going to take all our jobs - now it's taken mine." Microsoft is one of many tech companies experimenting with forms of so-called robot journalism to cut costs.

      In the Capitalism 4.0, rich still gets richer, and poor poorer. However, the current version doesn’t allow the poor to stay as poor enough to feed the rich as a consumer, as different to original capitalism concept of Ford.

    1. we expect the crisis to significantly increase the adoption of online and omnichannel delivery models.

      Send to Qianwen to use in the importance of omnichannel deliveries.

    2. After switching to daily planning cycles and gaining real-time visibility of their operations, managers don’t want to return to the old cadence of monthly planning and metrics that lag behind the situation on the ground. With

      We never go back. But we know that in wars, it is sometimes wiser to retract. When is the retract time for businesses? The most critical aim of a business to achieve continuous growth. There is no company with an ultimate growth aim, it is always endless. But does our situation allow this endless concept? Do we have endless resources, or the earth we are living on, does not its offering have a limit? Actually, it does. The world does not align with our eternal growth plans as it cannot bear with the increasing human population and their consumption behaviour. Nowadays, we are more aware of this reality and topics like climate change, global warming and environmental sustainability became in trend. If we regard the supporters of these concepts as the speaker of the world, who will direct us to change our endless growth targets and when?

    1. The best ideas, results, collaborations, and projects usually come when you think outside the box. You do not need to invest much time, money, or energy into this. The only thing you need is the desire and willingness to listen and understand a different point of view.

      Importance of analogy

    1. If you don’t know what decisions you want to make yet, the best you can do is go out there in search of inspiration. That’s called data-mining or analytics or descriptive analytics or exploratory data analysis (EDA) or knowledge discovery (KD)

      Scoping study

    2. mining

      Take this image

    3. Many

      Take the image

    4. perfectly

      Take the image

    5. definition

      Take the image

    1. Because subject matter expertise goes a long way towards helping you spot interesting patterns in your data faster, the best analysts are serious about familiarizing themselves with the domain. Failure to do so is a red flag. As their curiosity pushes them to develop a sense for the business, expect their output to shift from a jumble of false alarms to a sensibly-curated set of insights that decision-makers are more likely to care about.

      Analysts have domain expertise or knowledge at least.

    2. While statistical skills are required to test hypotheses, analysts are your best bet for coming up with those hypotheses in the first place. For instance, they might say something like “It’s only a correlation, but I suspect it could be driven by …” and then explain why they think that. This takes strong intuition about what might be going on beyond the data, and the communication skills to convey the options to the decision-maker, who typically calls the shots on which hypotheses (of many) are important enough to warrant a statistician’s effort. As analysts mature, they’ll begin to get the hang of judging what’s important in addition to what’s interesting, allowing decision-makers to step away from the middleman role.

      More formal and detailed version of above. Besides, the difference of being important and being interesting should be noted too. Maybe search for a thread.

    3. For example, not “we conclude” but “we are inspired to wonder”. They also discourage leaders’ overconfidence by emphasizing a multitude of possible interpretations for every insight.

      Data analysts are the inspiration team.

    4. Analysts are data storytellers. Their mandate is to summarize interesting facts and to use data for inspiration.

      This is actually what i do in my reviews too, so i may define myself as a qualitative analyst now.

    5. Excellence in analytics: speed The best analysts are lightning-fast coders who can surf vast datasets quickly, encountering and surfacing potential insights faster than those other specialists can say “whiteboard.” Their semi-sloppy coding style baffles traditional software engineers — but leaves them in the dust. Speed is their highest virtue, closely followed by the ability to identify potentially useful gems. A mastery of visual presentation of information helps, too: beautiful and effective plots allow the mind to extract information faster, which pays off in time-to-potential-insights. The result is that the business gets a finger on its pulse and eyes on previously-unknown unknowns. This generates the inspiration that helps decision-makers select valuable quests to send statisticians and ML engineers on, saving them from mathematically-impressive excavations of useless rabbit holes.

      Analysts are more of a digger, they carelessly and fast dig into data, maybe find some directions, which then will be studied elaborately by statisticians and then MLs to create sustainable and automated solutions.

    6. Performance means more than clearing a metric — it also means reliable, scalable, and easy-to-maintain models that perform well in production. Engineering excellence is a must. The result? A system that automates a tricky task well enough to pass your statistician’s strict testing bar and deliver the audacious performance a business leader demanded.

      What machine learners/ AIs do is to scale a statistically rigorous solution to a system-wide, complex problem.

    7. In other words, they use data to minimize the chance that you’ll come to an unwise conclusion.

      Role of statisticians

    1. The p-value says, “If I’m living in a world where I should be taking that default action, how unsurprising is my evidence?” The lower the p-value, the more the data are yelling, “Whoa, that’s surprising, maybe you should change your mind!”

      In a simpler context, it means the occurrence of default (null) situation is of very low probability.

    1. Other examples where society considers the default to be fairly obvious are innocent-until-proven-guilty (default = don’t convict if there’s no evidence), testing new medications (default = don’t approve if there’s no evidence), and scientific publication (default = don’t publish if there’s no evidence).

      As seen, the null hypothesis is of the non-impact situation

    2. What I’m asking you is, “What will you actually do if you walk away and remain ignorant of the information?”

      This is the null hypothesis, where there is no change in the situation or no further suggestion.

  2. May 2020
    1. In addition, the UK should take the opportunity to leverage its strong research and development base to identify globally competitive products and services, and evaluate where we have opportunities to make these products in the UK and export globally.


  3. Dec 2019