Also think about more local options and forums that have buy and sell theads. E.g. in the Netherlands we have the tweakers forum, which would be an ideal place for this.
Tessellecta
joined 2 years ago
My chemistry teacher once made salmiak as a demonstration. The only experiment we were ever allowed to taste and quite cool to look at too.
Probably an accident. It can't even physically swallow something of human size.
This is exactly why on most phones you can turn this feature off, which is also good to know.
If you are really really curious, you can find a phlebotomist that is game and use your own blood. This is the most ethical way to get some cooking blood and it can be done. (For proof see article)
https://www.vice.com/en/article/i-made-meringues-out-of-my-own-blood-and-ate-them/
You don't even need the movies to have some dystopian implant horror. Second sigh used to produce a sight restoring implant. After some financial trouble they stopped manufacturing and support for one of their products. Leaving recipients of the implant sightless in the case the hardware breaks.
Since they are antibiotic resistant bacteria, I have the suspicion that this is related to an increased use in antibiotics due to war injuries. Probably made worse by shortages that make people unable to finish the full regime all the time.
He is not quite a dictator yet. Let's call him an aspiring dictator, to make it clear that action can still prevent it from getting that bad.
No way he didn't know what het was doing. He hesitates before he does it the first time, then when he get's a positive reaction he does it the second time. This was deliberate and from what I can see many people in the the US are underreacting to it big time.
Funny thing, we actually call the calling someone jij tutoyeren and calling someone u vousvoyeren. This comes from the French.
Python
def read_input(path):
with open(path) as f:
lines = f.readlines()
for i, line in enumerate(lines):
ln = line.replace("\n","")
lines[i] = ln
return lines
def find_X(lines):
Xes = []
for j, line in enumerate(lines):
ind = [i for i, ltr in enumerate(line) if ltr == "X"]
for i in ind:
Xes.append((j,i))
return Xes
def find_M(lines, x, dim):
# Check for Ms
M_dirs = []
for i in [-1, 0, 1]:
x_ind = x[0] + i
if x_ind>=0 and x_ind<dim:
for j in [-1, 0, 1]:
y_ind = x[1]+j
if y_ind>=0 and y_ind<dim:
if lines[x_ind][y_ind] == "M":
M = [(x_ind, y_ind), (i,j)]
M_dirs.append(M)
return M_dirs
def check_surroundings(loc, lines, check_char, direction):
max = len(lines)-1
check_lock = [loc[i]+direction[i] for i in range(len(loc))]
if all(i>=0 and i<=max for i in check_lock) and check_char in str(lines[check_lock[0]][check_lock[1]]):
return True
else:
return False
def part_one(lines):
ans = 0
X = find_X(lines)
dim = len(lines[0])
for x in X:
M = find_M(lines, x, dim)
for m in M:
loc = m[0]
dir = m[1]
if not check_surroundings(loc, lines, 'A', dir):
continue
loc = [loc[0]+dir[0], loc[1]+dir[1]]
if not all(i>=0 and i<=dim-1 for i in loc):
continue
if not check_surroundings(loc, lines, 'S', dir):
continue
ans+=1
return ans
def extract_square(lines, loc):
str = ""
for i in range(-1,2,1):
for j in range(-1,2,1):
x_ind = loc[0]+i
y_ind = loc[1]+j
if not all(p>=0 and p<=len(lines[0])-1 for p in [x_ind, y_ind]):
raise ValueError("The given lock is at the edge of the grid and therefore will not produce a square")
str += lines[x_ind][y_ind]
return str
def check_square(square):
if not square[4]=="A":
return False
elif not ((square[0]=="M" and square[8]=="S") or (square[0]=="S" and square[8]=="M")):
return False
elif not ((square[2]=="M" and square[6]=="S") or (square[2]=="S" and square[6]=="M")):
return False
else: return True
def part_two(lines):
ans = 0
dim = len(lines[0])
for i in range(1,dim-1):
for j in range(1,dim-1):
square = extract_square(lines, (i,j))
if check_square(square):
ans += 1
return ans
path = r'Day_4\input.txt'
lines = read_input(path)
print("Answer part 1: ", part_one(lines))
print("Answer part 2: ", part_two(lines))
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I don't think that the forcing of an answer is the source of the problem you're describing. The source actually lies in the problems that the AI is taught to solve and the data it is provided to solve the problem.
In the case of medical image analysis, the problems are always very narrowly defined (e.g. segmenting the liver from an MRI image of scanner xyz made with protecol abc) and the training data is of very high quality. If the model will be used in the clinic, you also need to prove how well it works.
For modern AI chatbots the problem is: add one word to the end of the sentence starting with a system prompt, the data provided is whatever they could get on the internet, and the quality controle is: if it sounds good it is good.
Comparing the two problems it is easy to see why AI chatbots are prone to hallucination.
The actual power of the LLMs on the market is not as glorified google, but as foundational models that are used as pretraining for actual problems people want to solve.