5 ms·

What about this makes it an "AI robot machine" and not a "programmed machine"?

outworlder 3y ago
The article doesn't provide any details, but if I had to guess, absolutely nothing. But it gets more attention.
rich_sasha 3y ago
Not related but once I worked with a data provider who had an "AI matching system" for classifying strings. Turns out it was regular expressions.
- 9dev 3y ago
  I mean Regex is Turing complete, so technically…
  - Retric 3y ago
    Regular expressions aren’t necessarily Touring complete, some extensions to the standard are.
  - alwayslikethis 3y ago
    It isn't, at least in the proper sense. A regular language cannot be Turing complete.
    - sonicanatidae 3y ago
      It's an easy mistake to make. They both are most often used incorrectly by people that poorly understand them.
someotherperson 3y ago
In the past I've seen branding to the effect of 'Our product Appy opens a whole range of possibilities through a new, unique experience for data ingestion." It was just a REST API. "Appy" was quite literally just "API" read as a whole word.
__MatrixMan__ 3y ago
They're using predictive models to decide which experiment to do next. It's as AI as anything else. > The OER is a thermodynamically uphill reaction involving four consecutive oxidation steps and O–O bond formation, which requires an applied voltage of no less than 1.23 V to operate. The OER overpotential, which is defined as the extra voltage above 1.23 V required for catalysis to occur, characterizes the voltage efficiency of the electrochemical device. Therefore, we chose the measured overpotential as the primary target of our ML model in searching for the optimal OER catalyst10,11,12. We first created 29,902 unique compositions and simulated atomic structures of resulting high-entropy hydroxides (Fig. 2a) from classical MD simulations (Supplementary Figs. 11 and 12). The obtained structural features, such as averaged metal–metal and metal–oxygen distances (Supplementary Figs. 13 and 14 and Supplementary Table 2), are passed to previously established bimetallic hydroxide models13,14 (Fig. 2b) to determine the OER activity of each multimetallic hydroxide by DFT calculation. Three DFT-predicted OER activity descriptors—including the Gibbs free energy change of hydroxyl adsorption ΔGOH* (ref. 15) and differences between the Gibbs free energy change for oxygen adsorption and hydroxyl adsorption ΔGO−OH (ref. 16), the amount of charge transferred for hydroxyl adsorption on the activate site Δq (ref. 17)—and the paired composition information are used for NN training. As Fig. 2c shows, the NN model can accurately reproduce these DFT results. With the NN model, we can now rapidly predict the OER activity of high-entropy hydroxides obtained from any given composition of selected Martian ores (Supplementary Fig. 15), and these theoretical values are then connected with experimentally measured overpotentials. The ML model achieves remarkable accuracy in predicting true overpotentials (Fig. 2d).

The article doesn't provide any details, but if I had to guess, absolutely nothing. But it gets more attention.

Not related but once I worked with a data provider who had an "AI matching system" for classifying strings. Turns out it was regular expressions.

9dev 3y ago
I mean Regex is Turing complete, so technically…
- Retric 3y ago
  Regular expressions aren’t necessarily Touring complete, some extensions to the standard are.
- alwayslikethis 3y ago
  It isn't, at least in the proper sense. A regular language cannot be Turing complete.
  - sonicanatidae 3y ago
    It's an easy mistake to make. They both are most often used incorrectly by people that poorly understand them.

someotherperson 3y ago

In the past I've seen branding to the effect of 'Our product Appy opens a whole range of possibilities through a new, unique experience for data ingestion." It was just a REST API. "Appy" was quite literally just "API" read as a whole word.

__MatrixMan__ 3y ago

They're using predictive models to decide which experiment to do next. It's as AI as anything else. > The OER is a thermodynamically uphill reaction involving four consecutive oxidation steps and O–O bond formation, which requires an applied voltage of no less than 1.23 V to operate. The OER overpotential, which is defined as the extra voltage above 1.23 V required for catalysis to occur, characterizes the voltage efficiency of the electrochemical device. Therefore, we chose the measured overpotential as the primary target of our ML model in searching for the optimal OER catalyst10,11,12. We first created 29,902 unique compositions and simulated atomic structures of resulting high-entropy hydroxides (Fig. 2a) from classical MD simulations (Supplementary Figs. 11 and 12). The obtained structural features, such as averaged metal–metal and metal–oxygen distances (Supplementary Figs. 13 and 14 and Supplementary Table 2), are passed to previously established bimetallic hydroxide models13,14 (Fig. 2b) to determine the OER activity of each multimetallic hydroxide by DFT calculation. Three DFT-predicted OER activity descriptors—including the Gibbs free energy change of hydroxyl adsorption ΔGOH* (ref. 15) and differences between the Gibbs free energy change for oxygen adsorption and hydroxyl adsorption ΔGO−OH (ref. 16), the amount of charge transferred for hydroxyl adsorption on the activate site Δq (ref. 17)—and the paired composition information are used for NN training. As Fig. 2c shows, the NN model can accurately reproduce these DFT results. With the NN model, we can now rapidly predict the OER activity of high-entropy hydroxides obtained from any given composition of selected Martian ores (Supplementary Fig. 15), and these theoretical values are then connected with experimentally measured overpotentials. The ML model achieves remarkable accuracy in predicting true overpotentials (Fig. 2d).